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 .
Response to Arguments
Applicant's arguments filed have been fully considered but they are not persuasive for the following reasons:
Applicant argues on pg. 8:
“For at least the following reasons, Applicant respectfully submits that Tatani fails to teach or suggest the content evaluation device recited in claim 1, as amended herein…
… Paragraph [0297] of Tatani teaches that FIG. 42 is a schematic view illustrating the distinction of users of electronic apparatus. Paragraph [0298] of Tatani teaches that, when writing a letter or drawing a picture, the user (person/individual) is distinguished by the peculiarity of the movement and the peculiarity of the letter, wherein "peculiarity" refers to a movement speed, a moving interval, a cycle, handwriting, and the like of a pen when writing a letter or drawing a picture, and each of them is information having an individual-specific change pattern. Information corresponding to the "peculiarity" is calculated by the control part 41 from the value related to the movement detected by the sensor 6, and similarity determination and pattern matching with the information of the "peculiarity" of the individual registered in advance are performed, thereby, the user (person/individual) is distinguished. Notably, the user (person/individual) is not "content", as recited in claim 1.”
Examiner responds that Tatani’s para. 298 states “the user (person/individual) is distinguished by the peculiarity of the movement and the peculiarity of the letter...”. Examiner considers the written or drawn letter as content. It is Examiner’s position that Tatani’s system attempts to link a written/drawn letter to a specific user, which in essence authenticates the user as the author of the written/drawn letter. Therefore, the argument is rendered unpersuasive.
Applicant continues on pg. 8:
“Paragraph [0333] of Tatani teaches that FIG. 46 is a flowchart showing an example of correct answer confirmation processing. Paragraph [0335] of Tatani teaches that information of the correct answer is read in from the database (step S524), and the information obtained from the analysis result of the information of movement is compared with the information of the correct answer to determine whether or not the answer is correct (step S525). Paragraph [0336] of Tatani teaches that, if the answer is correct in this determination, a predetermined value is provided (step S526), and the processing proceeds to the next processing (step S527). Notably, Tatani fails to teach or suggest that the information of the correct answer that is read in from the database at step S524 includes a time-series feature corresponding to the correct answer.”
Examiner responds that characteristics such as speed and acceleration are “time-series” features, as claimed. It is clear that Tatani stores these features in order to compare to user input characteristics in the future (see para. 298, for example). Therefore, the argument is rendered unpersuasive.
Applicant continues on pg. 9:
“Paragraph [0371] of Tatani teaches that FIG. 51 is a flowchart showing an example of a signature discrimination processing. Paragraph [0372] of Tatani teaches that a signature is acquired in advance (step S571), wherein in order to register a correct signature, an electronic apparatus according to the present embodiment (for example, the pen-type electronic apparatus 1) is used and a user is asked to write a signature. Paragraph [0373] of Tatani teaches that the signature is analyzed (step S572), wherein information of the movement of the pen when the user writes the signature is detected. Paragraph [0374] of Tatani teaches that a signature for identity verification is acquired (step S574), wherein, when a credit card is used, the electronic apparatus (for example, the pen-type electronic apparatus 1) is used and the user is asked to write a signature for identity verification. Paragraph [0375] of Tatani teaches that the signature is analyzed (step S575), wherein this processing detects information of the movement of the pen when a signature for identity verification is written. Paragraph [0376] of Tatani teaches that signature matching is discriminated (step S576), wherein the degree of coincidence between the writing information of the correct signature stored in step S573 and the writing information of the signature for identity verification analyzed in step S575 is obtained. Paragraph [0377] of Tatani teaches that, when the degree of coincidence exceeds a predetermined value, the signature is coincident and authentication is OK (step S577). Paragraph [0378] of Tatani teaches that, in a signature discrimination processing, the discriminability is higher than that of handwriting collation since writing information (writing information including at least any of a writing direction, a writing speed, a writing acceleration, carefulness of the letter, a writing angle (the angle of the pen) and a pressure at the time of writing) obtained from the movement of the pen is used. Paragraph [0379] of Tatani teaches that the processing of the coincidence discrimination based on the writing information (step S576) may be performed by A.I., machine learning, deep learning, or the like. Notably, Tatani fails to teach or suggest that the correct signature stored in step S573 includes a time-series feature corresponding to the correct answer.”
Examiner responds that characteristics such as speed and acceleration are “time-series” features, as claimed. It is clear that Tatani stores these features in order to compare to user input characteristics in the future (see para. 298, for example). Therefore, the argument is rendered unpersuasive.
Claim Rejections - 35 USC § 102
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.
Claims 1 – 4 and 6 – 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tatani et al. (US Pub. No. 2021/0132709 A1).
As to claims 1 and 9, Tatani shows a content evaluation device and associated methodology (Figs. 1 and 46 and paras. 93 and 337) comprising: a processor (i.e. CPU 41, Fig. 2 and para. 96); and a memory storing a program (42, Fig. 2 and para. 96) that, when executed by the processor, causes the content evaluation device to: acquire at least one of content data indicating content composed of multiple content elements (i.e. a movement input via pen-type apparatus by a user, Fig. 46 and paras. 333, 334 and 337) or related data relating to creation of the content (i.e. amount of study time, for example, Fig. 46 and para. 338); calculate a time-series feature indicating a time change of a feature relating to a creation process of the content from the content data or the related data (i.e. amount of time studying and/or speed of writing, for example, Fig. 46 and paras. 336 – 338); and evaluate the content by using the time-series feature (i.e. evaluating of the content id correctly provided, Fig. 46 and para. 336), wherein authenticity of the content is determined based on a degree of similarity between a first time-series feature of an evaluation target and a second time-series feature corresponding to authenticated content (i.e. speed/acceleration, for example, Figs. 42 and 46 and paras. 298, 335 and 336).
As to claim 2, Tatani shows that the content data includes stroke data indicating an aggregate of strokes that are the content elements (i.e. writing input by the user, Fig. 46 and paras. 334, 337 and 338), and the program, when executed by the processor, causes the content evaluation device to evaluate a style of the content by using the time-series feature calculated from at least the stroke data (i.e. carefulness, for example, Fig. 46 and paras. 337 and 338).
As toc claim 3, Tatani shows that the related data includes biological data indicating a biological state of a creator at a time of the creation of the content (i.e. heart rate data, for example, Fig. 47 and paras. 348, 350 and 351), and the program, when executed by the processor, causes the content evaluation device to evaluate a psychological state of the creator by using the time-series feature calculated from at least the biological data (i.e. concentration, Fig. 47 and para. 351).
As to claim 4, Tatani shows that the related data includes environmental data indicating a state of an external environment at a time of the creation of the content (Fig. 44 and paras. 318 and 319), and the program, when executed by the processor, causes the content evaluation device to evaluate the state of the external environment by using the time-series feature calculated from at least the environmental data (Fig. 44 and paras. 318 and 319).
As to claim 5, Tatani shows that the program, when executed by the processor, causes the content evaluation device to obtain a degree of similarity between a first time-series feature corresponding to content of an evaluation target and a second time-series feature corresponding to authentic content (i.e. comparing user input to correct answer or a previous input, Figs. 46 and 42 and paras. 298, 335 and 336) and evaluates authenticity of the content of the evaluation target based on the degree of similarity (Figs. 42 and 46 and paras. 298, 335 and 336).
As to claim 6, Tatani shows that the program, when executed by the processor, causes the content evaluation device to normalize a plurality of times corresponding to each of the time-series features in a range from a start timing to an end timing of the creation of the content and calculates the time-series feature (Fig. 45 and paras. 324 – 332).
As to claim 7, Tatani shows that the system causes the content evaluation device to cut down a blank time included in a time interval of generation or editing of the content elements and calculates the time-series feature (Fig. 45 and paras. 324 – 332).
As to claim 8, Tatani shows non-transitory computer-readable medium storing a content evaluation program (Fig. 2 and para. 96) associated with a content evaluation device (Figs. 1 and 46 and paras. 93 and 337) comprising: a computer (i.e. CPU 41, Fig. 2 and para. 96); and a memory storing a program (42, Fig. 2 and para. 96) that, when executed by the computer, causes the content evaluation device to: acquire at least one of content data indicating content composed of multiple content elements (i.e. a movement input via pen-type apparatus by a user, Fig. 46 and paras. 333, 334 and 337) or related data relating to creation of the content (i.e. amount of study time, for example, Fig. 46 and para. 338); calculate a time-series feature indicating a time change of a feature relating to a creation process of the content from the content data or the related data (i.e. amount of time studying and/or speed of writing, for example, Fig. 46 and paras. 336 – 338); and evaluate the content by using the time-series feature (i.e. evaluating of the content id correctly provided, Fig. 46 and para. 336), wherein authenticity of the content is determined based on a degree of similarity between a first time-series feature of an evaluation target and a second time-series feature corresponding to authenticated content (i.e. speed/acceleration, for example, Figs. 42 and 46 and paras. 298, 335 and 336).
As to claim 10, Tatani shows a content evaluation system comprising a user device (i.e. pen 1, for example, Figs. 1 and 46 and paras. 93 and 337) that, in operation, generates content data indicating content composed of multiple content elements (i.e. letters or pictures, for example, Figs. 1 and 46 and paras. 94 and 337); and a server device that, in operation, communicates with the user device (para. 311), wherein the server device includes a processor (inherently the case) and memory storing a program (inherently the case) that, when executed by the processor, causes the server device to: acquire at least one of the content data (i.e. a movement input via pen-type apparatus by a user, Fig. 46 and paras. 333, 334 and 337) or related data relating to creation of the content from the user device (i.e. amount of study time, for example, Fig. 46 and para. 338); calculate a time-series feature indicating a time change of a feature relating to a creation process of the content from the content data or the related data (i.e. amount of time studying and/or speed of writing, for example, Fig. 46 and paras. 336 – 338); and evaluate the content by using the time-series feature (i.e. evaluating of the content id correctly provided, Fig. 46 and para. 336), wherein authenticity of the content is determined based on a degree of similarity between a first time-series feature of an evaluation target and a second time-series feature corresponding to authenticated content (i.e. speed/acceleration, for example, Figs. 42 and 46 and paras. 298, 335 and 336).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tatani in view of Damera-Venkata et al. (US Pub. No. 2014/0214751 A1).
As to claim 5, Tatani shows that the program, when executed by the processor, causes the content evaluation device to obtain the degree of similarity between the first time-series feature corresponding to content of the evaluation target and the second time-series feature corresponding to the authenticated content (i.e. speed/acceleration, for example, Figs. 42 and 46 and paras. 298, 335 and 336)
Tatani does not show that the similarity evaluation is based on a correlation coefficient.
Damera-Venkata shows the process of evaluating the similarity of content using coefficients (paras. 22 and 23).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the teachings of Tatani with those of Damera-Venkata because designing the system in this way allows the device to allow the collaborative filtering part of a mixed collaborative filtering-content analysis model to operate properly (para. 23).
CONCLUSION
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARL ADAMS whose telephone number is (571)270-7448. The examiner can normally be reached Monday - Friday, 9AM - 5PM EST.
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/CARL ADAMS/Examiner, Art Unit 2627
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