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:
“As best understood by the Applicant, the signature verification embodiment taught by Tatani compares writing information of a pre-registered correct signature with writing information of a signature for identity verification, and determines whether a signer is a legitimate person. In contrast, claim 1, as amended herein, recites that authenticity of an evaluation target content (i.e., not a user or signer) is determined, and also whether the evaluation target content is an authentic product or a counterfeit product of the authenticated content is determined…
…Even if Tatani uses writing information such as writing speed, writing acceleration, writing angle, pressure, and the like, Tatani fails to disclose constructing such information as an aggregate of data pairs indicating, for each content element, a correspondence between a feature of that content element and a time associated with generation or editing of that content element.
In particular, the signature verification taught by Tatani compares writing operation information for identity verification; it does not determine whether an evaluation target content is an authentic product or a counterfeit product of authenticated content based on feature-time data pairs associated with generation or editing of content elements.”
Examiner asserts that Tatani’s process of verifying whether a signature is consistent with a stored version that is known to be authentic is, in fact, a process of making a determination of whether an evaluation target content (i.e. the drawing/signature) is an authentic product or a counterfeit product of authenticated content, as recited in the claim. Also, since two versions of the content (i.e. the pairs) are compared with each other (i.e. the stored authenticated version against a recently produced version), Tatani’s process also shows that the methodology is based on feature-time data pairs associated with generation of content elements, as claimed. Therefore, the argument is rendered unpersuasive.
Applicant continues on pg. 9:
“In addition, the correct answer confirmation embodiment taught by Tatani appears to determine, in a learning-support context, whether a user's input is a correct answer. Claim 1, as amended herein, does not determine whether an answer is correct or incorrect. Instead, according to claim 1, as amended herein, whether an evaluation target content is an authentic product or a counterfeit product is determined based on a degree of similarity between the time- series feature associated with the evaluation target content and the time-series feature corresponding to the authenticated content.”
Examiner asserts that additional descriptions in Tatani shows the process of showing authentication, and not learning-support (see Fig. 51 and paras. 371 – 379 and rejection below). 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 or signature, for example, Figs. 46 and 51 paras. 338 and 371 – 379); 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, acceleration and/or speed of writing, for example, Figs. 46 and 51, and paras. 336 – 338 and 371 – 379); and evaluate the content by using the time-series feature (i.e. evaluating whether the content id correctly provided, Figs. 46 and 51 and paras. 336 and 371 – 379), wherein authenticity of an evaluation target content (i.e. a signature, for example) is determined based on a degree of similarity between a first time-series feature associated with the evaluation target content and a second time-series feature corresponding to authenticated content (i.e. speed/acceleration, for example, Figs. 42, 46 and 51 and paras. 298, 335, 336 and 371 – 379), wherein each of the first time-series feature and the second time-series feature is an aggregate of data pairs (i.e. priori signature/signature, Fig. 51 and paras. 371 – 379), wherein each of the data pairs indicates a correspondence between at least one feature of a content element of respective content and a time associated with generation or editing of the content element (i.e. priori signature was recorded in the past, Fig. 51 and paras. 371 – 379), and wherein the authenticity of the evaluation target content is determined by determining whether the evaluation target content is an authentic product or a counterfeit product of the authenticated content based on the degree of similarity (Fig. 51 and paras. 371 – 379).
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 associated with an evaluation target content 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 an evaluation target content (i.e. a signature, for example) is determined based on a degree of similarity between a first time-series feature associated with the evaluation target content and a second time-series feature corresponding to authenticated content (i.e. speed/acceleration, for example, Figs. 42, 46 and 51 and paras. 298, 335, 336 and 371 – 379), wherein each of the first time-series feature and the second time-series feature is an aggregate of data pairs (i.e. priori signature/signature, Fig. 51 and paras. 371 – 379), wherein each of the data pairs indicates a correspondence between at least one feature of a content element of respective content and a time associated with generation or editing of the content element (i.e. priori signature was recorded in the past, Fig. 51 and paras. 371 – 379), and wherein the authenticity of the evaluation target content is determined by determining whether the evaluation target content is an authentic product or a counterfeit product of the authenticated content based on the degree of similarity (Fig. 51 and paras. 371 – 379).
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 whether the content is 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 associated with an evaluation target content 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), wherein each of the first time-series feature and the second time-series feature is an aggregate of data pairs (i.e. priori signature/signature, Fig. 51 and paras. 371 – 379), wherein each of the data pairs indicates a correspondence between at least one feature of a content element of respective content and a time associated with generation or editing of the content element (i.e. priori signature was recorded in the past, Fig. 51 and paras. 371 – 379), and wherein the authenticity of the evaluation target content is determined by determining whether the evaluation target content is an authentic product or a counterfeit product of the authenticated content based on the degree of similarity (Fig. 51 and paras. 371 – 379).
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
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/CARL ADAMS/Examiner, Art Unit 2627
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