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 .
Response to Arguments
Applicant's arguments filed January 20, 2026 with regard to the 35 USC 101 rejection have been fully considered but they are not persuasive since the amended limitations do not provide enough for detail providing a technical improvement and a "significantly more". Applicant is welcomed to point out where in the specification that the Examiner can find support for providing a technical improvement and a "significantly more" according to the limitations claimed in the claim 1. In addition, “An image processing apparatus, comprising a processor, a memory, and a computer program that is stored in the memory and capable of being executed the processor to perform the “obtaining” step, “extract” step, “selecting” step and the “executing” step” is considered being performed by a generic computer. In addition, the limitation does it does not provide any details about how “obtaining” step, “extract” step, “selecting” step and the “executing” step are performed which is not able to be perform in human mind. Therefore, If the apparatus, processor and memory are removed from the claim, the method can be easily performed by a human being without the need of any of a computer component.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
“obtaining” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “executing” steps, with the “extract” step, the “obtaining” step, and the “selecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “extract” step, the “obtaining” step, the “selecting” step and “executing” step.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
Therefore, the 35 USC 101 rejections for claims 1-13 are maintained.
Response to Amendment
The amendment to the claims received on January 20, 2026 has been entered.
The amendment of claims 1, 7, 8, 12 and 13 is acknowledged
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.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception in the form of an Abstract Idea, without significantly more:
Beginning with independent claim 1, a device claim, which recites:
An image processing apparatus comprising: at least one memory device that stores a set of instructions; and at least one processor that executes the set of instructions to obtain image data generated by reading a sheet on which a handwritten character is written; extract one or more features from a handwritten character included in the image data; select a trained model to be used for character recognition of the handwritten from a plurality of trained models based on the extracted one or more features; and execute character recognition by using the selected trained model.
The claim recites abstract ideas:
“An image processing apparatus, comprising a processor, a memory, and a computer program that is stored in the memory and capable of being executed the processor to perform the “obtaining” step, “extract” step, “selecting” step and the “executing” step” is considered being performed by a generic computer. In addition, the limitation does it does not provide any details about how “obtaining” step, “extract” step, “selecting” step and the “executing” step are performed which is not able to be perform in human mind. Therefore, If the apparatus, processor and memory are removed from the claim, the method can be easily performed by a human being without the need of any of a computer component.
“obtaining” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “executing” steps, with the “extract” step, the “obtaining” step and “selecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “extract” step, the “obtaining” step, “selecting” step and “executing” step.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
Independent claim 12, a processing claim, which recites:
A method of controlling an image processing apparatus, the method comprising: obtaining image data generated by reading a sheet on which a handwritten character is written; extracting one or more features from a handwritten character included in the image data; selecting a trained model to be used for character recognition of the handwritten character from a plurality of trained models based on the extracted one or more features; and executing character recognition by using the selected trained model.
The claim recites abstract ideas:
“obtaining” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “executing” steps, with the “extract” step, the “obtaining” step and “selecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “extract” step, the “obtaining” step, “selecting” step and “executing” step.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
Independent claim 13, a device claim, which recites:
A non-transitory computer-readable storage medium storing program for causing a computer to execute each step of a method for controlling an image processing apparatus, the method comprising: obtaining image data generated by reading a sheet on which a handwritten character is written; extracting one or more features from a handwritten character included in the image data; selecting a trained model to be used for character recognition of the handwritten character from a plurality of trained models based on the extracted one or more features; and executing character recognition by using the selected trained model.
The claim recites abstract ideas:
“obtaining” is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
A process that encompass a human performing the steps mentally with or without a physical aid in the form of the “executing” steps, with the “extract” step, the “obtaining” step and “selecting” step being pre-solution acts of processing information which could be performed visually and/or mentally; and
A method of organizing human behavior in the form of a social activity of following rules or instructions informing a person to perform the “extract” step, the “obtaining” step, “selecting” step and “executing” step.
These two abstract ideas will be considered together for analysis as a single abstract idea per MPEP 2106:
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This judicial exception is not integrated into a practical application because there are no recited additional elements that amount to a practical application, such as but no limited to the following as noted in MPEP 2106:
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The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reason: There are not additional elements other than the abstract idea.
Independent claims 1, 12 and 13 is merely a generic computer implementation of the abstract ideas and likewise do not amount to significantly more. See MPEP 2106:
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Likewise, the following dependent claims have been analyzed and do not recite elements that recite a practical application or significantly more and remain rejected under 35 USC 101: Claims 2-11.
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.
Claims 1, 2, 6, 7, 9, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Karimi’205 (US 2021/0110205), and further in Huo’249 (US 2013/0251249) and AKHMAD’871 (RU 2419871 C2).
With respect to claim 1, Karimi’205 teaches an image processing apparatus (Fig. 1) comprising:
at least one memory device (Fig.1, item 104) that stores a set of instructions (page 7); and
at least one processor (Fig.1, item 102) that executes the set of instructions to obtain image data generated by reading a sheet on which a handwritten character is written (page 8 and (Fig.2, step S220);
select a trained model to be used for character recognition of the handwritten character on the sheet (Fig.2, step S220); and
execute character recognition by using the selected trained model (Fig.2, step 220).
Karimi’205 does not teach extract one or more features from a handwritten character included in the image data; select a trained model to be used for character recognition of the handwritten from a plurality of trained models based on the extracted one or more features.
Huo’249 teaches extract one or more features from a handwritten character [The extracted features of the incoming character are being obtained (paragraph 66). The handwritten character is the incoming character (paragraph 70)];
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Karimi’205 according to the teaching of Huo’249 to extract the features of the incoming character included in the image data and then to enable recognition module to selects one or more character classes according to the extracted features of the incoming character to perform character recognition for incoming character included in the image data because this will allow the character in the included in the image data to be recognized more effectively.
The combination of Karimi’205 and Huo’249 does not teach select a trained model to be used for character recognition of the handwritten from a plurality of trained models based on the extracted one or more features.
AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205 and Huo’249 according to the teaching of AKHMAD’871 to personalize the handwriting recognition application to generate a trained model for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user (selecting a trained model from a plurality of trained models) because this will allow the handwriting answering content from an image to be recognized more effectively.
The combination of Karimi’205, Huo’249 and AKHMAD’871 does not teach select a trained model to be used for character recognition of the handwritten from a plurality of trained models based on the extracted one or more features.
Since Karimi’205 teaches obtaining image data generated by reading a sheet on which a handwritten character is written (page 8 and (Fig.2, step S220), Huo’249 teaches the recognition module selects one or more character classes according to the extracted features of the incoming character to perform character recognition for incoming character (paragraph 66 and 67) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to recognize to personalize the handwriting recognition application to generate a trained model having the desired character classes for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user according to the extracted features of the handwriting answering content from the said image (select a trained model to be used for character recognition of the handwritten from a plurality of trained models based on the extracted one or more features) because this will allow the handwriting answering content from an image to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249 and AKHMAD’871 to personalize the handwriting recognition application to generate a trained model having the desired character classes for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user according to the extracted features of the handwriting answering content from the said image (select a trained model to be used for character recognition of the handwritten from a plurality of trained models based on the extracted one or more features) because this will allow the handwriting answering content from an image to be recognized more effectively.
With respect to claim 2, which further limits claim 1, the combination of Karimi’205 and Huo’249 does not teach wherein the at least one processor further executes the set of instructions to: obtain the plurality of trained models linked to different users which are generated by training using image data of handwritten characters of the respective user and corresponding ground truth data.
AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to recognize to enable a processor to obtain the plurality of trained models linked to different users which are being generated by training image data of handwritten characters of the respective user and corresponding ground truth data after the handwriting recognition application has personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) because this will allow each individual user’s handwriting to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205 and Huo’249 according to the teaching of AKHMAD’871 to generate a trained model for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user (selecting a trained model from a plurality of trained models) because this will allow the handwriting answering content from an image to be recognized more effectively.
With respect to claim 6, which further limits claim 2, the combination of Karimi’205, Huo’249 and AKHMAD’871 does not teach wherein the at least one processor further executes the set of instructions to: recognize a user based user identification information read from a predetermined region of the sheet, and select a trained model linked to the recognized user from the plurality of trained models, based on the recognized user.
Since Karimi’205 has suggest to recognize the handwriting on an image (Fig. 1) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) and user’s handwriting style is being used to identify the user’s information (pages 13 and 14), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after user’s handwriting style on a sheet is being read to determine the said user identification information (wherein the at least one processor further executes the set of instructions to: recognize a user based user identification information read from a predetermined region of the sheet, and select a trained model linked to the recognized user from the plurality of trained models, based on the recognized user) because this will allow the handwriting from a particular user to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249 and AKHMAD’871 to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after user’s handwriting style on a sheet is being read to determine the said user identification information (wherein the at least one processor further executes the set of instructions to: recognize a user based user identification information read from a predetermined region of the sheet, and select a trained model linked to the recognized user from the plurality of trained models, based on the recognized user) because this will allow the handwriting from a particular user to be recognized more effectively.
With respect to claim 7, which further limits claim 2, the combination of Karimi’205, AKHMAD’871 and Huo’249 does not teach wherein the at least one processor further executes the set of instructions to refine the selection of the training model by: extracting a feature of a handwritten character written on the sheet; and based on the extracted feature of the handwritten character, selecting from the plurality of trained models a trained model trained in correspondence with a feature determined to be similar to the extracted feature.
Since Karimi’205 teaches obtaining image data generated by reading a sheet on which a handwritten character is written (page 8 and (Fig.2, step S220), Huo’249 teaches the recognition module selects one or more character classes according to the extracted features of the incoming character to perform character recognition for incoming character (paragraph 66 and 67) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) and user’s handwriting style is being used to identify the user’s information (pages 13 and 14), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to recognize to personalize the handwriting recognition application to generate a trained model having the desired character classes for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user according to the extracted features of the handwriting answering content from the said image (wherein the at least one processor further executes the set of instructions to refine the selection of the training model by: extracting a feature of a handwritten character written on the sheet; and based on the extracted feature of the handwritten character, selecting from the plurality of trained models a trained model trained in correspondence with a feature determined to be similar to the extracted feature) because this will allow the handwriting answering content from an image to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249 and AKHMAD’871 to personalize the handwriting recognition application to generate a trained model having the desired character classes for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user according to the extracted features of the handwriting answering content from the said image (wherein the at least one processor further executes the set of instructions to refine the selection of the training model by: extracting a feature of a handwritten character written on the sheet; and based on the extracted feature of the handwritten character, selecting from the plurality of trained models a trained model trained in correspondence with a feature determined to be similar to the extracted feature) because this will allow the handwriting answering content from an image to be recognized more effectively.
With respect to claim 9, which further limits claim 2, Karimi’205 does not teach wherein the at least one processor further executes the set of instructions to: obtain the plurality of trained models from an external device capable of communicating with the image processing apparatus or an external storage capable of connecting with the image processing apparatus.
Since Karimi’205 has suggest to recognize the handwriting on an image (Fig. 1) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) and a remote memory storage device is also used to store program modules (page 8), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate personalized handwriting recognition models for each user and to store them on an remote memory storage device, and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: obtain the plurality of trained models from an external device capable of communicating with the image processing apparatus or an external storage capable of connecting with the image processing apparatus) because this will allow the handwriting from a particular user to be recognized more effectively.
With respect to claim 12, it is a method claim that claims how the image processing apparatus of claim 1 to recognize handwriting on an image. Claim 12 is obvious in view of Karimi’205, Huo’249 and AKHMAD’871 because the claimed combination operates at the same manner as described in the rejected claim 1. In addition, the reference has disclosed an image processing apparatus to recognize handwriting on an image, the process (method) to recognize handwriting on an image is inherent disclosed to be performed by a processor in the image processing apparatus when the image processing apparatus performs the operation to recognize handwriting on an image.
With respect to claims 13, it is a claim regarding to a non-transitory computer-readable storage medium storing thereon a computer program. Claim 13 claims how the image processing apparatus of claim 1 to recognize handwriting on an image. Claim 13 is obvious in view of Karimi’205, Huo’249 and AKHMAD’871 because the claimed combination operates at the same manner as described in the rejected claim 1. In addition, the reference discloses a process, the process would be implemented by a processor that requires a non-transitory computer readable medium, e.g., a RAM, to function, thus, the medium is inherently present
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Karimi’205 (US 2021/0110205), Huo’249 (US 2013/0251249), AKHMAD’871 (RU 2419871 C2) and further in view of
Morii’152 (US 2017/0013152).
With respect to claim 3, which further limits claim 2, the combination of Karimi’205, Huo’249 and AKHMAD’871 does not teach wherein the at least one processor further executes the set of instructions to: set a link between a logged-in user of the image processing apparatus and the obtained plurality of trained models, and select a trained model linked to the logged-in user of the image processing apparatus from the plurality of trained models.
Morii’152 teaches the MFP provide the function associated with the login user (paragraph 8).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249 and AKHMAD’871 according to the teaching of Morii’152 to associate the desired applications with users such that the desired application are being provided to the user after the user has login to the device because this will allow the desired applications to be provided to users more effectively.
The combination of Karimi’205, Huo’249, AKHMAD’871 and Morii’152 does not teach wherein the at least one processor further executes the set of instructions to: set a link between a logged-in user of the image processing apparatus and the obtained plurality of trained models, and select a trained model linked to the logged-in user of the image processing apparatus from the plurality of trained models.
Since Karimi’205 has suggest to recognize the handwriting on an image (Fig. 1) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) and Morii’152 teaches the MFP provide the function associated with the login user (paragraph 8), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: set a link between a logged-in user of the image processing apparatus and the obtained plurality of trained models, and select a trained model linked to the logged-in user of the image processing apparatus from the plurality of trained models) because this will allow the handwriting from a particular user to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249, AKHMAD’871 and Morii’152 to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: set a link between a logged-in user of the image processing apparatus and the obtained plurality of trained models, and select a trained model linked to the logged-in user of the image processing apparatus from the plurality of trained models) because this will allow the handwriting from a particular user to be recognized more effectively.
With respect to claim 4, which further limits claim 2, the combination of Karimi’205, Huo’249, AKHMAD’871 and Morii’152 does not teach wherein the at least one processor further executes the set of instructions to: set the trained model to be used for character recognition of handwritten characters of the sheet, based on a user operation, and select the set trained model from the plurality of trained models.
Morii’152 teaches the MFP provide the function associated with the login user (paragraph 8).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205 and AKHMAD’871 according to the teaching of Morii’152 to associate the desired applications with users such that the desired application are being provided to the user after the user has login to the device because this will allow the desired applications to be provided to users more effectively.
The combination of Karimi’205, Huo’249, AKHMAD’871 and Morii’152 does not teach wherein the at least one processor further executes the set of instructions to: set the trained model to be used for character recognition of handwritten characters of the sheet, based on a user operation, and select the set trained model from the plurality of trained models.
Since Karimi’205 has suggest to recognize the handwriting on an image (Fig. 1) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) and Morii’152 teaches the MFP provide the function associated with the login user (paragraph 8), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: set the trained model to be used for character recognition of handwritten characters of the sheet, based on a user operation, and select the set trained model from the plurality of trained models) because this will allow the handwriting from a particular user to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249, AKHMAD’871 and Morii’152 to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: set the trained model to be used for character recognition of handwritten characters of the sheet, based on a user operation, and select the set trained model from the plurality of trained models) because this will allow the handwriting from a particular user to be recognized more effectively.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Karimi’205 (US 2021/0110205), Huo’249 (US 2013/0251249), AKHMAD’871 (RU 2419871 C2) and further in view of
Mizuno’792 (US 2020/0195792).
With respect to claim 5, which further limits claim 2, the combination of Karimi’205, Huo’249, AKHMAD’871 does not teach wherein the at least one processor further executes the set of instructions to: set a link between a designated display language and the obtained plurality of trained models, and select a trained model linked to the designated display language from the plurality of trained models.
Mizuno’792 teaches that the MFP provides both of the designated display language and the functions associated with the user’s account after the user logins to the MFP provide with the login user (paragraph 58 and Fig.4).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249 and AKHMAD’871 according to the teaching of Mizuno’792 to associate the desired applications and display language with users such that the desired application and the display language are being provided to the user after the user has login to the device because this will allow the desired applications to be provided to users more effectively.
The combination of Karimi’205, Huo’249, AKHMAD’871 and Mizuno’792 does not teach wherein the at least one processor further executes the set of instructions to: set a link between a designated display language and the obtained plurality of trained models, and select a trained model linked to the designated display language from the plurality of trained models.
Since Karimi’205 has suggest to recognize the handwriting on an image (Fig. 1) and AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16) and Mizuno’792 teaches the MFP provides both of the designated display language and the functions associated with the user’s account after the user logins to the MFP provide with the login user (paragraph 58 and Fig.4), therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models with the desired display language to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: set a link between a designated display language and the obtained plurality of trained models, and select a trained model linked to the designated display language from the plurality of trained models) because this will allow the handwriting from a particular user to be recognized more effectively.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249, AKHMAD’871 and Morii’152 to generate personalized handwriting recognition models for each user and then to provide the associated generated personalized handwriting recognition model selected from the plurality of generated personalized handwriting recognition models with the desired display language to a user after the said user has login to the system (wherein the at least one processor further executes the set of instructions to: set a link between a designated display language and the obtained plurality of trained models, and select a trained model linked to the designated display language from the plurality of trained models) because this will allow the handwriting from a particular user to be recognized more effectively.
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Karimi’205 (US 2021/0110205), Huo’249 (US 2013/0251249), AKHMAD’871 (RU 2419871 C2) and further in view of Tai-ru’175 (CN 112990175).
With respect to claim 8, which further limits claim 7, Karimi’205 does not teaches wherein the at least one processor further executes the set of instructions to extract the feature using a trained model: based on the extracted a feature of a handwritten character, further specialize the selection by selecting from the plurality of trained models a trained model trained in correspondence with a feature determined to be similar to the extracted feature.
Tai-ru’175 teaches wherein the at least one processor further executes the set of instructions to: extract a feature of a handwritten character written on a sheet using a trained model, and based on the extracted feature of the handwritten character, select a trained model trained in correspondence with a feature determined to be similar to the feature (Fig. 4).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Karimi’205 according to the teaching of Tai-ru’175 to extract a feature of a handwritten character written on a sheet to perform character recognition because this will allow the handwriting on a paper to be recognized more effectively.
The combination of Karimi’205, Huo’249 and Tai-ru’175 does not teach plurality of trained models are being provided.
AKHMAD’871 teaches the handwriting recognition application was personalized for each person by training a generalized handwriting recognition application from the original training set (page 16).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249 and Tai-ru’175 according to the teaching of AKHMAD’871 to personalize the handwriting recognition application to generate a trained model for each user such that a desire trained model associated with a user is being selected from a plurality of trained model to recognize the handwriting answering content from an image associated with the said user (selecting a trained model from a plurality of trained models) because this will allow the handwriting answering content from an image to be recognized more effectively.
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Karimi’205 (US 2021/0110205), Huo’249 (US 2013/0251249), AKHMAD’871 (RU 2419871 C2) and further in view of Oda’442 (US 2012/0213442).
With respect to claim 10, which further limits claim 1, the combination of Karimi’205, Huo’249 and AKHMAD’871 does not teach wherein the at least one processor further executes the set of instructions to: obtain image data from the sheet by using a reading unit that the image processing apparatus is provided with.
Oda’442 teaches wherein the at least one processor further executes the set of instructions to: obtain image data from the sheet by using a reading unit that the image processing apparatus is provided with (paragraph 46).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249, AKHMAD’871 according to the teaching of Oda’442 to include a scanner scan a paper having handwriting to provide an image handwriting to perform character recognition because this will allow the handwriting on a paper to be recognized more effectively.
With respect claim 11, which further limits claim 1, Karimi’205 does not teach wherein the at least one processor further executes the set of instructions to: obtains image data read from the sheet by an external device.
Oda’442 teaches wherein the at least one processor further executes the set of instructions to: obtains image data read from the sheet by an external device (paragraph 46).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Karimi’205, Huo’249, AKHMAD’871 according to the teaching of Oda’442 to include a scanner scan a paper having handwriting to provide an image handwriting to perform character recognition because this will allow the handwriting on a paper to be recognized more effectively.
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 extension fee 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 date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUO LONG CHEN whose telephone number is (571)270-3759. The examiner can normally be reached on M-F 9am - 5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tieu, Benny can be reached on (571) 272-7490. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HUO LONG CHEN/Primary Examiner, Art Unit 2682