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
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 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang et al. (US 20180075198 A1) in view of Kang et al. (KR 10-2521594 B1), and further in view of Zaneti (US 20120135386 A1).
Regarding claim 1, Hwang et al. teaches comprising: a step (S100) of inputting information about an electronic child drawing to a controller (100), by a client computer (120) (see para [0013]; “A drawing 30 is scanned and input into the drawing analyzer 12 for analysis”); a step (S300) of generating analysis data by performing a predetermined analysis on the pre-processed child drawing based on an object and a color, by the controller (100) (see para [0014]; “The drawing digitizer 14 performs analytics to determine visual characteristics of the drawing. The characteristics include data relating to color, objects, location”); a step (S400) of calculating an interpretation indicator by inputting the analysis data to a service model by the controller (100) (see para [0015]; “The method further includes step S107 automatically interpreting the color, object, location, time and mood characteristics to determine the mental state of the user”). However, Hwang et al. does not teach a pre-processing step (S200) of removing a predetermined object from the child drawing, by the controller (100); and. a step (S500) of generating and outputting an analysis report based on the interpretation indicator, by the controller (100).
In the same field of endeavor, Kang et al. teaches a child drawing understanding and analysis reporting method (see Abstract; “child who is the user who created picture data based on a word cloud, and to objectively determine information about the colors mainly used to create the picture data in creating the picture data by the child through the degree of use of color, and to objectively determine the characteristics of the drawing data created by the user, the child, through the color characteristic distribution map”), a pre-processing step (S200) of removing a predetermined object from the child drawing, by the controller (100) (see page 2, 5th para; “the artificial intelligence server removes the background image from the picture data; c) generating result data by extracting colors from the picture data through analysis of the picture data by the artificial intelligence server”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a method for automatically assessing the mental state of a user from a drawing made by the user in view of a system that analyzes picture colors and analyzes users based on artificial intelligence of Kang et al. in order to objectively determine the characteristics of the drawing data created by the user (see Abstract). However, the combination of Hwang et al. and Kang et al. as a whole does not teach and. a step (S500) of generating and outputting an analysis report based on the interpretation indicator, by the controller (100).
In the same field of endeavor, Zaneti teaches and. a step (S500) of generating and outputting an analysis report based on the interpretation indicator, by the controller (100) (see para [0022]; “psychological characteristics corresponding to currently obtained and preprocessed drawing data are extracted from the data base. The extracted characteristics are subsequently output--e.g. in the form of a report”, see also para [00082]; “Finally the program issues a report, summarizing the diagnostic findings. The report may include references to related information and suggestions for further actions, including references to professional help”).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Hwang et al. in the combination further teach wherein the input step (S100) includes at least one of: a step (S110) of inputting a file of the child drawing; a step (S120) of inputting a metafile of the child drawing; a step (S130) of inputting meta information about a child who draws the child drawing; and a step (S140) of inputting feedback information about the child or the child drawing, generated by a teacher of the child (see para [0029]; “a profile and events data module 44 is used to create a profile for the user which may include data such as age, gender, and other related personal data. … The data input from module 44 will allow the state of user assessment module 38 to compare the drawing analyzer data with the user information in determining normal or abnormal patterns and other affects on the user mental state”, see also claim 1; “receiving a profile of the user including personal user data and user events data”, and para [0035]; “Care giver or user tags 48 may be input for the drawing”).
Regarding claim 3, the rejection of claim 1 is incorporated herein.
Kang et al. in the combination further teach wherein in the pre-processing step (S200), the predetermined object is at least one of a teacher’s drawing drawn by the teacher, a background drawing, and a white background color if the drawing is drawn by the child and the teacher together (see page 2, 5th para; “a child user inputting picture data using an input module; b) the data server of the analysis module transmits the picture data input to the input module to the artificial intelligence server, and the artificial intelligence server removes the background image from the picture data; c) generating result data by extracting colors from the picture data through analysis of the picture data by the artificial intelligence server”).
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Hwang et al. in the combination further teach wherein the analysis data generating step (S330) includes at least one of: a step (S310) of recognizing an object in the child drawing; a step (S320) of recognizing colors of the child drawing; a step (S330) of recognizing lines of the child drawing; a step (S340) of recognizing spaces of the child drawing (see para [0004]; “the mental state of the user is automatically assessed by digitizing a drawing and determining drawing characteristics, which can include color(s), object(s), and location”, see also para [0014]; “The characteristics include data relating to color, objects, location, placement and composition, lines, marks, weight, style, people, faces, flowers, florals, geometric shapes, random abstract shapes, houses, windows, 3D boxes, ladders, arrows, walls, stars and hearts”); a step (S350) of analyzing behavior data of the child; to generate the analysis data (see para [0035]; “The state of assessment module 38 extracts patterns of behavior from the machine learning data and determines the mental state of the user based on the drawing analysis trained through historical data with correct tags”)
Regarding claim 6, the rejection of claim 5 is incorporated herein.
Hwang et al. in the combination further teach wherein in the step (S310) of recognizing objects of the child drawing, the number of objects and sizes of the objects are recognized (see para [0014]; “The color analyzer 18 considers number of colors used, the most dominant colors and other color characteristics. The size analyzer 20 considers the objects presented, the position on the paper, the relative size of the objects to each other and other size related characteristics. The location analyzer 22 considers the relative grouping of objects to each other, the location being depicted by the drawing and other location related characteristics”); in the step (S340) of recognizing spaces of the child drawing, at least one of an occupancy ratio and a placement of the object is recognized (see para [0014]; “The characteristics include data relating to color, objects, location, placement and composition”), and in the step (S350) of analyzing behavior data of a child, at least one of a drawing hour and the number of corrections is analyzed (see para [0014]; “The time analyzer 16 considers how much time the user needed to complete the drawing and other time related characteristics input to the drawing analyzer 12”).
Kang et al. in the combination further teach and in the step (S320) of recognizing colors of the child drawing, at least one of a coloring ratio, a primary color, a primary color usage ratio, a hue type, a tone ratio, and a color usage difference (std) are recognized (see page 5, 5th para; “correct the color (color) applied to the image (picture or photo) with the standard color, and then extract the color by calculating the ratio of colors close to the corresponding color”, see also page 12, para 16th; “After that, the artificial intelligence server 220 generates a color image scale (S1423), displays one or more color regions 321 to 325 constituting each cluster on the color image scale, and then determines the size ratio of each cluster. Accordingly, the color characteristic distribution map 320 may be generated by adjusting the size of the color regions 321 to 325 (S1424)”), in the step (S330) of recognizing lines of the child drawing, at least one of a pen pressure, a length, a thickness, a repetition, the number of times, and a speed of the line is recognized (see page 7, para 13th; “The offline input device 120 transmits picture data 105 directly drawn by the user, that is, offline using a tool (eg, a brush, pen, etc.) to the data server 210”, see page 9, last para; “a difference occurs in the length of the first to fifth brush touches 311a to 315a according to the size of each cluster sorted by the artificial intelligence server 220. The length may be shortened toward the brush touch 315a”).
Regarding claim 7, the rejection of claim 1 is incorporated herein.
Hwang et al. in the combination further teach wherein the interpretation indicator calculating step (S400) includes at least one of: a step (S410) of diagnosing and testing the child drawing by a drawing diagnosis test model (141); a step (S420) of clinically classifying the child drawing, by a child clinical classification model (142); a step (S430) of classifying a general tendency of the child who draws the child drawing, by a child general tendency classification model (143); a step (S440) of analyzing a development of the child drawing, by a child drawing development model (144); and a step (S450) of analyzing the child drawing, by a child drawing analysis model (145) (see para [0019]; “The module 29 may also include a categorical database to diagnose the mental state of drawers. The state of user assessment module 38 takes in all the inputs from the drawing analyzer 12 and in some cases caregiver input 39, and automatically determines the mental state of the user. The assessment module 38 may illustrate the different mental states with different parameter values”, see also para [0022]; “The mental state or states detected may, for example, include anger, anxiety, abuse, loss of loved person, depression and others states. The mental state detected may be the personality characteristics of the user”, see also para [0035]; “a machine learning module 46 to enable learning about the user's development patterns. Care giver or user tags 48 may be input for the drawing. The state of assessment module 38 extracts patterns of behavior from the machine learning data and determines the mental state of the user based on the drawing analysis trained through historical data with correct tags. As shown in FIG. 3, the method includes step S114 using a machine learning algorithm to learn developmental patterns. The developmental patterns are combined in step S115 with the step S107 in determining the mental state of the user”).
Zanetic in the combination further teach and a step (S460) of storing and outputting an interpretation indicator output from the service model (see para [00082]; “Finally the program issues a report, summarizing the diagnostic findings. The report may include references to related information and suggestions for further actions, including references to professional help”, see also para [0021]; “During the interpretation subphase, parameter values and results of the preprocessing subphase are examined in association with a stored database, which, in effect, incorporates relationships between drawing parameters and any data derivable therefrom, on the one hand, and psychological characteristics of corresponding subjects, on the other hand… The method has two distinct application modes, characterized in the interpretation subphase--normal mode and study mode. In the normal mode, psychological characteristics corresponding to currently obtained and preprocessed drawing data are extracted from the data base. The extracted characteristics are subsequently output--e.g. in the form of a report”).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Hwang et al. and Kang et al, in view of Zaneti as applied in claim 1, and 3 above, and further in view of Woolf et al. (US 20040237033 A1) and Nishioka (US 20080266612 A1).
Regarding claim 4, the rejection of claim 3 is incorporated herein.
Kang et al. in the combination further teach wherein the pre-processing step (S200) includes: a step (S220) of removing a background drawing from the child drawing (see page 2, 5th para; “the artificial intelligence server removes the background image from the picture data”), a step (S230) of removing a background color from the child drawing (see page 12, 6th para; “the background image has been removed by similar colors (S1411)”). However, the combination of Hwang et al., Kang et al. and Zaneti as a whole does not teach a step (S210) of removing a teacher's drawing drawn by the teacher from the child drawing when the child and the teacher draw the drawing together; and a step (S240) of assigning a penalty to an achromatic color in the child drawing.
In the same field of endeavor, Woolf et al. teaches a step (S210) of removing a teacher's drawing drawn by the teacher from the child drawing when the child and the teacher draw the drawing together (see para [0009]; “A user may also filter the view of the shared annotations”, see also para [0042]; “the shared canvas mode is not strictly anonymous, since any user can choose to only show one layer of ink at a time using the display filter”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a method for automatically assessing the mental state of a user from a drawing made by the user in view of a system that analyzes picture colors and analyzes users based on artificial intelligence of Kang et al. and a system employing electronic inking capabilities of Woolf et al. in order to determine who is subscribing to those notes (see para [0009]. However, the combination of Hwang et al., Kang et al., Zaneti and Woolf et al. as a whole does not teach and a step (S240) of assigning a penalty to an achromatic color in the child drawing.
In the same field of endeavor, Nishioka teaches and a step (S240) of assigning a penalty to an achromatic color in the child drawing (see Abstract; “Boundary lines extending from the determined background color area to an achromatic-color area are generated. The background color area and an area surrounded by the background color area and the boundary lines are set as a mask, and the background color of the original document is deleted”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a method for automatically assessing the mental state of a user from a drawing made by the user in view of a system that analyzes picture colors and analyzes users based on artificial intelligence of Kang et al. and a system for digitally relating psychological characteristics of a subject to on-screen drawings of Zaneti and further in view of a system employing electronic inking capabilities of Woolf et al. and an image processing device detects a candidate area of a background color in a chromaticity plane of Nishioka in order to extract and remove a background color area that is disposed away from the center of the chromaticity plane (see Abstract).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang et al. and Kang et al. in view of Zaneti as applied in claim 1, and 7 above and further in view of Yeon et al. (KR 2022-0108451 A), Kuk et al. (KR 2022-0124592 A) and Goodman et al. NPL “Drawings from a play-based intervention: Windows to the soul of rural Ugandan preschool children’s artistic development”.
Regarding claim 8, the rejection of claim 7 is incorporated herein.
Hwang et al. in the combination further teach wherein the drawing diagnosis test model outputs a stress indicator and a copying resource indicator as the interpretation indicator (see Abstract; “The mental state of the user is automatically determined by interpreting the color, object, location, time and mood characteristics and to automatically select a user action”, see also para [0039]; “categories of the assessment analysis include various states of mind and drawing types. In addition, the output can be multiple with different probabilities. The analysis categories may include: [0038] Emotions: Deal with emotions like anger and sadness through these helpful exercises. [0039] Relaxation: Level of stress or relaxation”), the child clinical classification model includes a depression indicator, an anxiety indicator, (see para [0022]; “The mental state or states detected may, for example, include anger, anxiety, abuse, loss of loved person, depression and others states. The mental state detected may be the personality characteristics of the user”). However, the combination of Hwang et al., Kang et al. and Zaneti as a whole does not teach an ADHD indicator as the interpretation indicator, and an immersion indicator as the interpretation indicator; the child general tendency classification model includes a realistic indicator, an exploratory indicator, and an artistic indicator as the interpretation indicator; the child drawing development model includes a scribbling stage indicator, a preschematic stage indicator, and a schematic stage indicator, as the interpretation indicator, and the child drawing analysis model includes an aesthetic sensitivity indicator.
In the same field of endeavor, Yeon et al. teaches and an ADHD indicator as the interpretation indicator (see page 3, 2nd para; “each art education content can be classified by characteristics such as problem-solving ability, sociality, creativity, fine motor development, concentration, memory, cognitive development, emotional development, stress relief, attention (e.g., attention deficit hyperactivity disorder, ADHD)”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a method for automatically assessing the mental state of a user from a drawing made by the user in view of a system that analyzes picture colors and analyzes users based on artificial intelligence of Kang et al. and a system for digitally relating psychological characteristics of a subject to on-screen drawings of Zaneti and further in view of method for platform-based art education and consultation of Yeon et al. in order to provide customized content reflecting attributes of the subject (see page 3, 2nd para). However, the combination of Hwang et al., Kang et al. Zaneti and Kim as a whole does not teach the child general tendency classification model includes a realistic indicator, an exploratory indicator, and an artistic indicator as the interpretation indicator; the child drawing development model includes a scribbling stage indicator, a preschematic stage indicator, and a schematic stage indicator, as the interpretation indicator, and the child drawing analysis model includes an aesthetic sensitivity indicator.
In the same field of endeavor, Kuk et al. teach the child general tendency classification model includes a realistic indicator, an exploratory indicator, and an artistic indicator as the interpretation indicator, and an immersion indicator as the interpretation indicator (see page 14, 4th para “The is a test written based on Holland's interest type theory, and it is a test that develops the preferred interest type of children's interest test a child. Specifically, the child's interest test is classified into realistic, inquiry, artistic, social, enterprising, and conventional types based on Holland's theory of interest types”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a method for automatically assessing the mental state of a user from a drawing made by the user in view of a system that analyzes picture colors and analyzes users based on artificial intelligence of Kang et al. and a system for digitally relating psychological characteristics of a subject to on-screen drawings of Zaneti and further in view of method for platform-based art education and consultation of Yeon et al. and online psychological test system and online psychological test method of Kuk et al. in order to easily maintain and manage the online psychological test system (see page 14, 4th para). The combination of Hwang et al., Kang et al. Zaneti, Kim and Kuk et al. as a whole does not teach the child drawing development model includes a scribbling stage indicator, a preschematic stage indicator, and a schematic stage indicator, as the interpretation indicator, and the child drawing analysis model includes an aesthetic sensitivity indicator.
In the same field of endeavor, Goodman et al. teaches the child drawing development model includes a scribbling stage indicator, a preschematic stage indicator, and a schematic stage indicator, as the interpretation indicator, and the child drawing analysis model includes an aesthetic sensitivity indicator (see page 3, left col. 4th para; “for analyzing the characteristics of children’s artistic development from infancy to late adolescence are as follows: The Scribbling Stage, The Preschematic Stage, The Schematic Stage, The Gang Stage, The Pseudo-Naturalistic Stage, and finally, Adolescent Art”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a method for automatically assessing the mental state of a user from a drawing made by the user in view of a system that analyzes picture colors and analyzes users based on artificial intelligence of Kang et al. and a system for digitally relating psychological characteristics of a subject to on-screen drawings of Zaneti and further in view of method for platform-based art education and consultation of Yeon et al. and online psychological test system and online psychological test method of Kuk et al. and drawings from a play-based intervention of Goodman et al. in order to demonstrate important deviations from Lowenfeld’s developmental stages model (see page 3, left col. 4th para).
Regarding claim 9, the rejection of claim 8 is incorporated herein.
Zanetic in the combination further teach wherein the analysis report output step (S500) includes: a step of performing at least one of: a step (S510) of generating a drawing diagnosis test report (161); a step (S520) of generating a child emotion/psychology analysis report (162); a step (S530) of generating a child tendency/carrier path analysis report (163); a step (S540) of generating a child artistic tendency report (164); and a step (S550) of generating an Art BONGBONG growth report (165), based on the interpretation indicator; and a step (S560) of outputting the generated report(see para [00082]; “Finally the program issues a report, summarizing the diagnostic findings. The report may include references to related information and suggestions for further actions, including references to professional help”, see also para [0021]; “During the interpretation subphase, parameter values and results of the preprocessing subphase are examined in association with a stored database, which, in effect, incorporates relationships between drawing parameters and any data derivable therefrom, on the one hand, and psychological characteristics of corresponding subjects, on the other hand… The method has two distinct application modes, characterized in the interpretation subphase--normal mode and study mode. In the normal mode, psychological characteristics corresponding to currently obtained and preprocessed drawing data are extracted from the data base. The extracted characteristics are subsequently output--e.g. in the form of a report”).
Regarding claim 10, the rejection of claim 9 is incorporated herein.
Hwang et al. in the combination further teach wherein the drawing diagnosis test report (161) (see para [0021]; “results of the preprocessing subphase are examined in association with a stored database…. The extracted characteristics are subsequently output--e.g. in the form of a report”).
Yeon et al. the child artistic tendency report (164) and the Art BONGBONG growth report (165) are based on the interpretation indicators of the drawing diagnosis test model (see page 10, 9th para; “art education content related to problem-solving ability may include child psychology and work analysis products. For another example, art education content related to creativity growth or fine muscle development may include an expert's analysis report product”, see also page 10, last para; “experts and/or AI algorithms (eg, deep learning, etc.) are based on the results of art education performed by the subject for a certain period. ..Thus, analysis results can be derived”), the child drawing development model, and the child drawing analysis model, respectively and the child emotion/psychology analysis report (162) (see page 10, last para; “the analysis result of the expert may include information about the subject's description of the subject's picture, the subject's psychological state determined based on the picture, and the subject's ability determined based on the picture”) and the child tendency/career path analysis report (163) are based on the interpretation indicators of the child clinical classification model (142) and the child general tendency classification model (143), respectively (see page 11, 1st para; “the expert may provide outstanding abilities and competencies that require supplementation”).
Conclusion
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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
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