DETAILED ACTION
This office action is in response to communication(s) filed on October 23, 2025.
Claims 10-11 have been cancelled.
Claims 1 and 12-13 have been amended.
Claims 1-9 and 12-13 are currently pending.
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(s) 1-6 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Terada (U.S. Pub. No. 2021/0306513 A1), Natale (U.S. Pub. No. 2014/0270400 A1, hereinafter as “Natale”), Tokuda (U.S. Pub. No. 2018/0218232 A1), and further in view of Misawa (U.S. Pub. No. 2020/0134309 A1)
With regard to claim 1, the claim is drawn to an information processing apparatus (Terada, i.e. in Fig. 1 and etc., disclose the image forming apparatus) comprising:
a processor (Terada, i.e. in Fig. 1 and etc., disclose the image forming apparatus, and further fig 1 and in para. 20, disclose that “[0020] The processor 31 is an arithmetic element (for example, a CPU) that executes arithmetic processing. The processor 31 is the main body of the operation of the system controller 21. The processor 31 performs various processes based on data such as programs or the like stored in the memory 32. The processor 31 functions as a control unit that can execute various operations by executing a program stored in the memory 32. The processor 31 is configured with a lower specification than the processor installed in the cloud server 12. As a result, the cost of the image forming apparatus 11 can be suppressed.”) configured to:
obtain image data (see Terada, i.e. in fig. 1 and para. 25, disclose “image reading unit 25”, and “[0025] The image reading unit 25 reads an image from a paper sheet. The image reading unit 25 includes, for example, a scanner and an automatic document feeder (ADF). The scanner includes an image sensor and an optical system. The scanner images the reflected light of the light emitted to the paper sheet on the image sensor by the optical system. The scanner reads the electric charge accumulated in the image sensor by the light imaged by the optical system, converts the electric charge into a digital signal, and generates image data of the paper sheet. The scanner reads the paper sheet from the side of the glass plate on which the paper sheet is arranged that faces the paper sheet. The image reading unit 25 acquires an image of the entire paper sheet by acquiring an image with the scanner while moving the scanner. Further, the image reading unit 25 acquires an image of the entire paper sheet by acquiring an image with the scanner while passing the paper sheet through the reading position of the scanner by the ADF, for example.”);
obtain information including at least one of setting information set in advance for optical character recognition processing by a plurality of apparatuses capable of communicating with the information processing apparatus or attribute information of each of the plurality of apparatuses (see Terada, i.e. in para. fig. 1, para. 13, 17, 46-49, claim 9 and etc., disclose that “[0013] At least one embodiment relates to an example in which, in the image processing system 1, the image forming apparatus 11 transmits image data to the cloud server 12 and executes optical character recognition (OCR) processing based on the image data received by the cloud server 12”, “[0017] The cloud server 12 is a server device including a processor, a memory, and a communication interface. The cloud server 12 executes image processing (e.g., OCR processing) by the processor executing a program in the memory. For example, when the cloud server 12 receives the image data from the image forming apparatus 11, the cloud server 12 analyzes the image data by the OCR processing and generates a document file based on the character recognition result. The cloud server 12 transmits the generated document file to the image forming apparatus 11.”, and “…[0049] The above first autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12. In the first autoencoder model, the number of units in the intermediate layer is set so that an image with precision required for OCR processing in the cloud server 12 can be output.”; additionally in claim 9, disclose “the plurality of autoencoder models…”), wherein each of the plurality of apparatuses is physically separate from each other (see teachings of Natale supplemented below); and
based on the obtained image data and the obtained information, divide the image data into a plurality of portions, analyze each of the plurality of portions to identify characteristics of each of the plurality of portions, and determine an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatuses based on respective characteristics of each of the plurality of portions (see Terada, i.e. in para. 46-51 and etc., disclose that “[0046] The image forming apparatus 11 and the cloud server 12 are configured to support a plurality of different autoencoder models. That is, the encoder 28 of the image forming apparatus 11 can encode image data by any of a plurality of encoding methods corresponding to a plurality of different autoencoder models. Further, the decoder 43 of the cloud server 12 can decode the intermediate layer data by any of a plurality of decoding methods corresponding to the plurality of different autoencoder models. [0047] In at least one embodiment, the encoder 28 is configured to be able to perform encoding by any of a first encoding method corresponding to a first autoencoder model, a second encoding method corresponding to a second autoencoder model, and a third encoding method corresponding to a third autoencoder model. [0048] Also, the decoder 43 is configured to be able to perform decoding by any of a first decoding method corresponding to the first autoencoder model, a second decoding method corresponding to the second autoencoder model, and a third decoding method corresponding to the third autoencoder model. [0049] The above first autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12. In the first autoencoder model, the number of units in the intermediate layer is set so that an image with precision required for OCR processing in the cloud server 12 can be output. … [0051] Further, the third autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12 and correcting the OCR processing result by the operator (e.g., using an operator entry). In the operator entry, since the operator checks the output image and corrects the OCR processing result, the precision of the output image may be minimum. Therefore, in the third autoencoder model, the number of units in the intermediate layer is set so that the image with the minimum precision in the cloud server 12 can be output. Therefore, the third encoder model is set to have a smaller number of units (e.g., data capacity) of intermediate layer data than that of the first encoder model. As a result, the third autoencoder model can minimize the capacity of the intermediate layer data.”; also see the teachings of Tokuda and Misawa supplemented below in interest of advancing the prosecution).
The teaching of Terada, among others, disclose that, in para. 46, that “[0046] The image forming apparatus 11 and the cloud server 12 are configured to support a plurality of different autoencoder models. That is, the encoder 28 of the image forming apparatus 11 can encode image data by any of a plurality of encoding methods corresponding to a plurality of different autoencoder models. Further, the decoder 43 of the cloud server 12 can decode the intermediate layer data by any of a plurality of decoding methods corresponding to the plurality of different autoencoder models”; however, teachings of Terada do not explicitly disclose the aspect with regard to “wherein each of the plurality of apparatuses is physically separate from each other”.
However, Natale disclose an analogous invention relates to online checking-in for international flights and, more particularly, to the use of electronically stored travel document information to expedite the check-in process. More specifically, i.e. in fig. 1A, para. 22 and etc., disclose that hundreds of OCR servers 170 are interconnected, by disclosing that “[0022] Although the electronic boarding pass system 100 is shown to include one application server 140, one OCR server 170, and one government server 172 in communication with one of each of the devices 206-216 it should be understood that different numbers of application servers 140, OCR servers 170, government servers 172, and devices 206-216 may be utilized. For example, the digital network 130 (or other digital networks, not shown) may interconnect in the system 100 a plurality of included application servers 140, hundreds of OCR servers 170, and thousands of devices 206-216. According to the disclosed example, this configuration may provide several advantages, such as, for example, enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This provides for a primary backup of all the information generated in the electronic boarding pass system 100. Alternatively, some of the devices 206-216 may store data locally”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terada to include the limitation(s) discussed and also taught by Natale, with the aspect discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to document and/or character recognizing/processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terada by the teachings of Natale, and to incorporate the limitation(s) discussed and also taught by Natale, thereby “...the OCR server 170 may employ known algorithms to determine that the image contains an MRZ, ascertain the format of the MRZ, and extract the text in the MRZ from the image. If the OCR server 170 was able to extract the text from the MRZ (block 910), the OCR server 170 may transmit the extracted text to the device 212 and/or the application server 140 to relay to the device 212 (block 912)” (see Natale, i.e. in para. 50 and etc.).
In addition, in interest of advancing the prosecution and also in response to applicant’s argument(s) (i.e. arguments A and B from the remarks filed on May 2, 2025) relating to claimed limitation(s) of “… and the obtained information, analyze the obtained image data to identify characteristics of the image data and determine an apparatus used for optical character recognition processing of the image data from among the plurality of apparatuses”, examiner further supplement the teachings of Tokuda as following.
Tokuda discloses an analogous invention relates to providing an image processing apparatus and an image processing method which can properly use a plurality of OCR processes (see Tokuda, i.e. para. 12 and etc.). More specifically, in Tokuda, in fig. 4, 5, para. 85, 86 and etc., disclose that “[0085] The image processing apparatus 1 may be configured to have a memory which has settings (OCR settings) whether to give priority to the internal OCR process, whether to give priority to the cloud OCR process, or whether to switch between the internal OCR process and the cloud OCR process in accordance with the layout of the original document image data [or as claimed “…analyze the obtained image data to identify characteristics of the image data and determine an apparatus used for optical character recognition processing of the image data…”]. For example, the image processing apparatus 1 has the OCR settings inside the non-volatile memory 64. The CPU 61 may be configured to change the OCR settings in accordance with the operation signal input via the operation I/F 21 or the control signal input via the communication I/F 22. [or as claimed “… obtain information including at least one of setting information set in advance for optical character recognition processing by a plurality of apparatus capable of communicating with ….”]”.
[0086] FIG. 4 is a view for describing an example of the OCR settings. For example, the OCR settings have setting items such as “automatic”, “priority to speed”, and “priority to reproducibility” [or as “at least one of setting information” claimed]. In an example in FIG. 4, “automatic” is selected. For example, when “automatic” is selected as the OCR settings, the CPU 61 switches between the internal OCR process and the cloud OCR process in accordance with the layout of the original document image data. For example, when “priority to speed” is selected as the OCR settings, the CPU 61 generates the document file from the original document image data by performing the internal OCR process without depending on the layout of the original document image data. For example, when “priority to reproducibility” is selected as the OCR settings, the CPU 61 generates the document file from the original document image data by performing the cloud OCR process without depending on the layout of the original document image data.”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada and Natale to include the limitation(s) discussed and also taught by Tokuda, with respect to the aspect(s) discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to image processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada and Natale by the teachings of Tokuda, and to incorporate the limitation(s) discussed and also taught by Tokuda, thereby “…to provide an image processing apparatus and an image processing method which can properly use a plurality of OCR processes” (see Tokuda, i.e. para. 12 and etc.).
With regards to the amended aspects relating to “… divide the image data into plurality of portions, …. and determine an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatus based on respective characteristics of each of the plurality of portions”, in interest of advancing the prosecution, examiner further submits the teachings of Misawa as following.
Misawa discloses an analogous invention relates to an image processing apparatus, a method for controlling the image processing apparatus, and a storage medium (see Misawa, i.e. para. 1 and etc.). More specifically, in Misawa, i.e. in fig. 4, step S405 and correspondingly in para. 78, discloses that “[0078] In step S405, the CPU 201 performs the region determination processing on the binary image data generated in step S403, to detect character regions and photo image region.” [or as claimed “dividing the image data into plurality of portions…”]. Further, in Fig. 4, steps S406 and S409, and in para. 79, 82 and etc., disclose that “[0079] In step S406, the CPU 201 performs the first OCR processing on one of the regions determined as the character region in step S405 to obtain character codes, and stores the obtained character codes in the RAM 203”, and “[0082] In step S409, the CPU 201 performs the second OCR processing on the character region having been subjected to the second binarization processing of the binary image data generated in step S408, and stores the obtained character codes in the RAM 203” [or as claimed “…. determine an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatus based on respective characteristics of each of the plurality of portions”].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada, Natale and Tokuda to include the limitation(s) discussed and also taught by Misawa, with respect to the aspect(s) discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to image and/or document processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada, Natale and Tokuda by the teachings of Misawa, and to incorporate the limitation(s) discussed and also taught by Misawa, thereby to have “… improvement of a recognition rate of a character included in the read image in the image processing apparatus that does not continuously store the multivalued image data before the binarization” (see Misawa, i.e. para. 7 and etc.).
With regard to claim 2, the claim is drawn to the information processing apparatus according to Claim 1, wherein the setting information in a case where the setting information is included in the information is information on billing (see Terada, i.e. in para. 66-67, discloses that “[0066] For example, the image forming apparatus 11 may have setting items such as “cost priority”, “precision priority”, “communication load priority”, and “balance”. The processor 31 of the system controller 21 of the image forming apparatus 11 may be configured to select one of the “standard OCR processing”, the “option A”, the “option B”, and the “option C” based on these settings and the service registration status. [0067] For example, when “cost priority” is selected, the processor 31 selects “standard OCR processing” that requires the least cost. When “precision priority” is selected, the processor 31 selects “option B (high-precision OCR processing)”. When “communication load priority” is selected, the processor 31 selects “option C (OCR processing + operator entry)”. When “balance” is selected, the processor 31 selects “option A (OCR processing)”.
With regard to claim 3, the claim is drawn to the information processing apparatus according to Claim 2, wherein the information on billing is an acceptable upper limit value per page (see Terada, i.e. in para. 66-67, discloses that “[0066] For example, the image forming apparatus 11 may have setting items such as “cost priority”, “precision priority”, “communication load priority”, and “balance”. The processor 31 of the system controller 21 of the image forming apparatus 11 may be configured to select one of the “standard OCR processing”, the “option A”, the “option B”, and the “option C” based on these settings and the service registration status. [0067] For example, when “cost priority” is selected, the processor 31 selects “standard OCR processing” that requires the least cost. When “precision priority” is selected, the processor 31 selects “option B (high-precision OCR processing)”. When “communication load priority” is selected, the processor 31 selects “option C (OCR processing + operator entry)”. When “balance” is selected, the processor 31 selects “option A (OCR processing)”.
With regard to claim 4, the claim is drawn to the information processing apparatus according to Claim 2, wherein, in a case where the plurality of apparatuses include an apparatus having a fixed fee until a number of pages on which the optical character recognition processing is performed exceeds a predetermined value, the information on billing is information indicating a number of pages on which the optical character recognition processing has been performed or information indicating a number of pages until the predetermined value is reached (see Terada, i.e. in para. 23-24, 52-55 and etc., disclose that “[0023] The display 23 displays a screen according to a video signal input from the display control unit such as the system controller 21 or a graphic controller (not shown). For example, the display 23 displays screens for various settings of the image forming apparatus 11.[0024] The operation interface 24 (e.g., an operator interface, a user interface) includes various operation members (e.g., configured to receive operator inputs or user inputs). The operation interface 24 supplies an operation signal corresponding to the operation of the operation member to the system controller 21. The operation member is, for example, a touch sensor, a numeric keypad, a power key, a paper feed key, various function keys, or a keyboard. The touch sensor is, for example, a resistive film type touch sensor, a capacitance type touch sensor, or the like. The touch sensor acquires information indicating a designated position in a certain area. The touch sensor is configured as a touch panel integrally with the display 23 to input a signal indicating the touched position on the screen displayed on the display 23 to the system controller 21.[0052] Next, a method of determining the type of OCR processing to be executed by the cloud server 12 will be described. FIG. 3 is an explanatory diagram for illustrating an example of a screen regarding the OCR processing displayed on the image forming apparatus 11. [0053] The processor 31 of the system controller 21 of the image forming apparatus 11 causes the display 23 to display an OCR processing selection screen 61 shown in FIG. 3. The processor 31 determines OCR processing to be executed by the cloud server 12 and an autoencoder model (e.g., an encoding method) used for the encoding in accordance with an operation on the OCR processing selection screen 61. [0054] On the OCR processing selection screen 61, a “standard OCR processing” button 62, an “option A” button 63, an “option B” button 64, an “option C” button 65, a “setting” button 66, and a “cancel” button 67, and the like are displayed. [0055] The “standard OCR processing” button 62 is a button for selecting the execution of OCR processing by the image forming apparatus 11. When the “standard OCR processing” button 62 is selected, the processor 31 executes the OCR processing based on the acquired image data, using the OCR algorithm recorded in the memory 32 in advance.”).
With regard to claim 5, the claim is drawn to the information processing apparatus according to Claim 1, wherein the attribute information in a case where the attribute information is included in the information is information on characters included in the image data (see Terada, i.e. in para. 82, discloses that “[0082] Further, the image data is an image including characters, and the processor 31 acquires, from the cloud server 12, the result of the OCR processing executed on the output image acquired by the cloud server 12 decoding the intermediate layer data. As a result, the processor 31 can reduce the communication load and cause the cloud server 12 to execute the OCR processing….”).
With regard to claim 6, the claim is drawn to the information processing apparatus according to Claim 5, wherein the information on characters included in the image data is information on a notation aspect of the characters (see Terada, i.e. in para. 82 and etc., disclose that “[0082] Further, the image data is an image including characters, and the processor 31 acquires, from the cloud server 12, the result of the OCR processing executed on the output image acquired by the cloud server 12 decoding the intermediate layer data. As a result, the processor 31 can reduce the communication load and cause the cloud server 12 to execute the OCR processing”).
With regard to claim 12, the claim is drawn to a non-transitory computer readable medium storing a program causing an information processing apparatus to execute a process (see Terada, i.e. in para. 20, discloses that “[0020] The processor 31 is an arithmetic element (for example, a CPU) that executes arithmetic processing. The processor 31 is the main body of the operation of the system controller 21. The processor 31 performs various processes based on data such as programs or the like stored in the memory 32. The processor 31 functions as a control unit that can execute various operations by executing a program stored in the memory 32. The processor 31 is configured with a lower specification than the processor installed in the cloud server 12. As a result, the cost of the image forming apparatus 11 can be suppressed.”), the process comprising:
obtaining image data (see Terada, i.e. in fig. 1 and para. 25, disclose “image reading unit 25”, and “[0025] The image reading unit 25 reads an image from a paper sheet. The image reading unit 25 includes, for example, a scanner and an automatic document feeder (ADF). The scanner includes an image sensor and an optical system. The scanner images the reflected light of the light emitted to the paper sheet on the image sensor by the optical system. The scanner reads the electric charge accumulated in the image sensor by the light imaged by the optical system, converts the electric charge into a digital signal, and generates image data of the paper sheet. The scanner reads the paper sheet from the side of the glass plate on which the paper sheet is arranged that faces the paper sheet. The image reading unit 25 acquires an image of the entire paper sheet by acquiring an image with the scanner while moving the scanner. Further, the image reading unit 25 acquires an image of the entire paper sheet by acquiring an image with the scanner while passing the paper sheet through the reading position of the scanner by the ADF, for example.”);
obtaining information including at least one of setting information set in advance for optical character recognition processing by a plurality of apparatuses capable of communicating with the information processing apparatus or attribute information of each of the plurality of apparatuses (see Terada, i.e. in para. fig. 1, para. 13, 17, 46-49, claim 9 and etc., disclose that “[0013] At least one embodiment relates to an example in which, in the image processing system 1, the image forming apparatus 11 transmits image data to the cloud server 12 and executes optical character recognition (OCR) processing based on the image data received by the cloud server 12”, “[0017] The cloud server 12 is a server device including a processor, a memory, and a communication interface. The cloud server 12 executes image processing (e.g., OCR processing) by the processor executing a program in the memory. For example, when the cloud server 12 receives the image data from the image forming apparatus 11, the cloud server 12 analyzes the image data by the OCR processing and generates a document file based on the character recognition result. The cloud server 12 transmits the generated document file to the image forming apparatus 11.”, and “…[0049] The above first autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12. In the first autoencoder model, the number of units in the intermediate layer is set so that an image with precision required for OCR processing in the cloud server 12 can be output.”; additionally in claim 9, disclose “the plurality of autoencoder models…”), wherein each of the plurality of apparatuses is physically separate from each other (see teachings of Natale supplemented below); and
based on the obtained image data and the obtained information, dividing the image data into a plurality of portions, analyzing each of the plurality of portions to identify characteristics of each of the plurality of portions, and determining an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatuses based on respective characteristics of each of the plurality of portions (see Terada, i.e. in para. 46-51 and etc., disclose that “[0046] The image forming apparatus 11 and the cloud server 12 are configured to support a plurality of different autoencoder models. That is, the encoder 28 of the image forming apparatus 11 can encode image data by any of a plurality of encoding methods corresponding to a plurality of different autoencoder models. Further, the decoder 43 of the cloud server 12 can decode the intermediate layer data by any of a plurality of decoding methods corresponding to the plurality of different autoencoder models. [0047] In at least one embodiment, the encoder 28 is configured to be able to perform encoding by any of a first encoding method corresponding to a first autoencoder model, a second encoding method corresponding to a second autoencoder model, and a third encoding method corresponding to a third autoencoder model. [0048] Also, the decoder 43 is configured to be able to perform decoding by any of a first decoding method corresponding to the first autoencoder model, a second decoding method corresponding to the second autoencoder model, and a third decoding method corresponding to the third autoencoder model. [0049] The above first autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12. In the first autoencoder model, the number of units in the intermediate layer is set so that an image with precision required for OCR processing in the cloud server 12 can be output. … [0051] Further, the third autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12 and correcting the OCR processing result by the operator (e.g., using an operator entry). In the operator entry, since the operator checks the output image and corrects the OCR processing result, the precision of the output image may be minimum. Therefore, in the third autoencoder model, the number of units in the intermediate layer is set so that the image with the minimum precision in the cloud server 12 can be output. Therefore, the third encoder model is set to have a smaller number of units (e.g., data capacity) of intermediate layer data than that of the first encoder model. As a result, the third autoencoder model can minimize the capacity of the intermediate layer data.”; also see the teachings of Tokuda supplemented below in interest of advancing the prosecution).
The teaching of Terada, among others, disclose that, in para. 46, that “[0046] The image forming apparatus 11 and the cloud server 12 are configured to support a plurality of different autoencoder models. That is, the encoder 28 of the image forming apparatus 11 can encode image data by any of a plurality of encoding methods corresponding to a plurality of different autoencoder models. Further, the decoder 43 of the cloud server 12 can decode the intermediate layer data by any of a plurality of decoding methods corresponding to the plurality of different autoencoder models”; however, teachings of Terada do not explicitly disclose the aspect with regard to “wherein each of the plurality of apparatuses is physically separate from each other”.
However, Natale disclose an analogous invention relates to online checking-in for international flights and, more particularly, to the use of electronically stored travel document information to expedite the check-in process. More specifically, i.e. in fig. 1A, para. 22 and etc., disclose that hundreds of OCR servers 170 are interconnected, by disclosing that “[0022] Although the electronic boarding pass system 100 is shown to include one application server 140, one OCR server 170, and one government server 172 in communication with one of each of the devices 206-216 it should be understood that different numbers of application servers 140, OCR servers 170, government servers 172, and devices 206-216 may be utilized. For example, the digital network 130 (or other digital networks, not shown) may interconnect in the system 100 a plurality of included application servers 140, hundreds of OCR servers 170, and thousands of devices 206-216. According to the disclosed example, this configuration may provide several advantages, such as, for example, enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This provides for a primary backup of all the information generated in the electronic boarding pass system 100. Alternatively, some of the devices 206-216 may store data locally”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terada to include the limitation(s) discussed and also taught by Natale, with the aspect discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to document and/or character recognizing/processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terada by the teachings of Natale, and to incorporate the limitation(s) discussed and also taught by Natale, thereby “...the OCR server 170 may employ known algorithms to determine that the image contains an MRZ, ascertain the format of the MRZ, and extract the text in the MRZ from the image. If the OCR server 170 was able to extract the text from the MRZ (block 910), the OCR server 170 may transmit the extracted text to the device 212 and/or the application server 140 to relay to the device 212 (block 912)” (see Natale, i.e. in para. 50 and etc.).
In interest of advancing the prosecution and also in response to applicant’s argument(s) (i.e. arguments A and B from the remarks filed on May 2, 2025) relating to claimed limitation(s) of “… and the obtained information, analyze the obtained image data to identify characteristics of the image data and determine an apparatus used for optical character recognition processing of the image data from among the plurality of apparatuses”, examiner further supplement the teachings of Tokuda as following.
Tokuda discloses an analogous invention relates to providing an image processing apparatus and an image processing method which can properly use a plurality of OCR processes (see Tokuda, i.e. para. 12 and etc.). More specifically, in Tokuda, in fig. 4, 5, para. 85, 86 and etc., disclose that “[0085] The image processing apparatus 1 may be configured to have a memory which has settings (OCR settings) whether to give priority to the internal OCR process, whether to give priority to the cloud OCR process, or whether to switch between the internal OCR process and the cloud OCR process in accordance with the layout of the original document image data [or as claimed “…analyze the obtained image data to identify characteristics of the image data and determine an apparatus used for optical character recognition processing of the image data…”]. For example, the image processing apparatus 1 has the OCR settings inside the non-volatile memory 64. The CPU 61 may be configured to change the OCR settings in accordance with the operation signal input via the operation I/F 21 or the control signal input via the communication I/F 22. [or as claimed “… obtain information including at least one of setting information set in advance for optical character recognition processing by a plurality of apparatus capable of communicating with ….”]”.
[0086] FIG. 4 is a view for describing an example of the OCR settings. For example, the OCR settings have setting items such as “automatic”, “priority to speed”, and “priority to reproducibility” [or as “at least one of setting information” claimed]. In an example in FIG. 4, “automatic” is selected. For example, when “automatic” is selected as the OCR settings, the CPU 61 switches between the internal OCR process and the cloud OCR process in accordance with the layout of the original document image data. For example, when “priority to speed” is selected as the OCR settings, the CPU 61 generates the document file from the original document image data by performing the internal OCR process without depending on the layout of the original document image data. For example, when “priority to reproducibility” is selected as the OCR settings, the CPU 61 generates the document file from the original document image data by performing the cloud OCR process without depending on the layout of the original document image data.”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada and Natale to include the limitation(s) discussed and also taught by Tokuda, with respect to the aspect(s) discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to image processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada and Natale by the teachings of Tokuda, and to incorporate the limitation(s) discussed and also taught by Tokuda, thereby “…to provide an image processing apparatus and an image processing method which can properly use a plurality of OCR processes” (see Tokuda, i.e. para. 12 and etc.).
With regards to the amended aspects relating to “… divide the image data into plurality of portions, …. and determine an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatus based on respective characteristics of each of the plurality of portions”, in interest of advancing the prosecution, examiner further submits the teachings of Misawa as following.
Misawa discloses an analogous invention relates to an image processing apparatus, a method for controlling the image processing apparatus, and a storage medium (see Misawa, i.e. para. 1 and etc.). More specifically, in Misawa, i.e. in fig. 4, step S405 and correspondingly in para. 78, discloses that “[0078] In step S405, the CPU 201 performs the region determination processing on the binary image data generated in step S403, to detect character regions and photo image region.” [or as claimed “dividing the image data into plurality of portions…”]. Further, in Fig. 4, steps S406 and S409, and in para. 79, 82 and etc., disclose that “[0079] In step S406, the CPU 201 performs the first OCR processing on one of the regions determined as the character region in step S405 to obtain character codes, and stores the obtained character codes in the RAM 203”, and “[0082] In step S409, the CPU 201 performs the second OCR processing on the character region having been subjected to the second binarization processing of the binary image data generated in step S408, and stores the obtained character codes in the RAM 203” [or as claimed “…. determine an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatus based on respective characteristics of each of the plurality of portions”].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada, Natale and Tokuda to include the limitation(s) discussed and also taught by Misawa, with respect to the aspect(s) discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to image and/or document processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Terada, Natale and Tokuda by the teachings of Misawa, and to incorporate the limitation(s) discussed and also taught by Misawa, thereby to have “… improvement of a recognition rate of a character included in the read image in the image processing apparatus that does not continuously store the multivalued image data before the binarization” (see Misawa, i.e. para. 7 and etc.).
With regard to claim 13, the claim is drawn to an information processing method (see Terada, i.e. in para. 9, discloses that “an image forming apparatus and a control method of an image forming apparatus, which can reduce a communication load”) comprising:
obtaining image data (see Terada, i.e. in fig. 1 and para. 25, disclose “image reading unit 25”, and “[0025] The image reading unit 25 reads an image from a paper sheet. The image reading unit 25 includes, for example, a scanner and an automatic document feeder (ADF). The scanner includes an image sensor and an optical system. The scanner images the reflected light of the light emitted to the paper sheet on the image sensor by the optical system. The scanner reads the electric charge accumulated in the image sensor by the light imaged by the optical system, converts the electric charge into a digital signal, and generates image data of the paper sheet. The scanner reads the paper sheet from the side of the glass plate on which the paper sheet is arranged that faces the paper sheet. The image reading unit 25 acquires an image of the entire paper sheet by acquiring an image with the scanner while moving the scanner. Further, the image reading unit 25 acquires an image of the entire paper sheet by acquiring an image with the scanner while passing the paper sheet through the reading position of the scanner by the ADF, for example.”);
obtaining information including at least one of setting information set in advance for optical character recognition processing by a plurality of apparatuses capable of communicating with an information processing apparatus or attribute information of each of the plurality of apparatuses (see Terada, i.e. in para. fig. 1, para. 13, 17, 46-49, claim 9 and etc., disclose that “[0013] At least one embodiment relates to an example in which, in the image processing system 1, the image forming apparatus 11 transmits image data to the cloud server 12 and executes optical character recognition (OCR) processing based on the image data received by the cloud server 12”, “[0017] The cloud server 12 is a server device including a processor, a memory, and a communication interface. The cloud server 12 executes image processing (e.g., OCR processing) by the processor executing a program in the memory. For example, when the cloud server 12 receives the image data from the image forming apparatus 11, the cloud server 12 analyzes the image data by the OCR processing and generates a document file based on the character recognition result. The cloud server 12 transmits the generated document file to the image forming apparatus 11.”, and “…[0049] The above first autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12. In the first autoencoder model, the number of units in the intermediate layer is set so that an image with precision required for OCR processing in the cloud server 12 can be output.”; additionally in claim 9, disclose “the plurality of autoencoder models…”), wherein each of the plurality of apparatuses is physically separate from each other (see teachings of Natale supplemented below); and
based on the obtained image data and the obtained information, dividing the image data into a plurality of portions, analyzing each of the plurality of portions to identify characteristics of each of plurality of portions, and determining an apparatus used for optical character recognition processing for each portion of the image data from among the plurality of apparatuses based on respective characteristic of each of the plurality of portions (see Terada, i.e. in para. 46-51 and etc., disclose that “[0046] The image forming apparatus 11 and the cloud server 12 are configured to support a plurality of different autoencoder models. That is, the encoder 28 of the image forming apparatus 11 can encode image data by any of a plurality of encoding methods corresponding to a plurality of different autoencoder models. Further, the decoder 43 of the cloud server 12 can decode the intermediate layer data by any of a plurality of decoding methods corresponding to the plurality of different autoencoder models. [0047] In at least one embodiment, the encoder 28 is configured to be able to perform encoding by any of a first encoding method corresponding to a first autoencoder model, a second encoding method corresponding to a second autoencoder model, and a third encoding method corresponding to a third autoencoder model. [0048] Also, the decoder 43 is configured to be able to perform decoding by any of a first decoding method corresponding to the first autoencoder model, a second decoding method corresponding to the second autoencoder model, and a third decoding method corresponding to the third autoencoder model. [0049] The above first autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12. In the first autoencoder model, the number of units in the intermediate layer is set so that an image with precision required for OCR processing in the cloud server 12 can be output. … [0051] Further, the third autoencoder model is a model for performing OCR processing based on the output image in the cloud server 12 and correcting the OCR processing result by the operator (e.g., using an operator entry). In the operator entry, since the operator checks the output image and corrects the OCR processing result, the precision of the output image may be minimum. Therefore, in the third autoencoder model, the number of units in the intermediate layer is set so that the image with the minimum precision in the cloud server 12 can be output. Therefore, the third encoder model is set to have a smaller number of units (e.g., data capacity) of intermediate layer data than that of the first encoder model. As a result, the third autoencoder model can minimize the capacity of the intermediate layer data.”; also see the teachings of Tokuda supplemented below in interest of advancing the prosecution).
The teaching of Terada, among others, disclose that, in para. 46, that “[0046] The image forming apparatus 11 and the cloud server 12 are configured to support a plurality of different autoencoder models. That is, the encoder 28 of the image forming apparatus 11 can encode image data by any of a plurality of encoding methods corresponding to a plurality of different autoencoder models. Further, the decoder 43 of the cloud server 12 can decode the intermediate layer data by any of a plurality of decoding methods corresponding to the plurality of different autoencoder models”; however, teachings of Terada do not explicitly disclose the aspect with regard to “wherein each of the plurality of apparatuses is physically separate from each other”.
However, Natale disclose an analogous invention relates to online checking-in for international flights and, more particularly, to the use of electronically stored travel document information to expedite the check-in process. More specifically, i.e. in fig. 1A, para. 22 and etc., disclose that hundreds of OCR servers 170 are interconnected, by disclosing that “[0022] Although the electronic boarding pass system 100 is shown to include one application server 140, one OCR server 170, and one government server 172 in communication with one of each of the devices 206-216 it should be understood that different numbers of application servers 140, OCR servers 170, government servers 172, and devices 206-216 may be utilized. For example, the digital network 130 (or other digital networks, not shown) may interconnect in the system 100 a plurality of included application servers 140, hundreds of OCR servers 170, and thousands of devices 206-216. According to the disclosed example, this configuration may provide several advantages, such as, for example, enabling near real-time uploads and downloads of information as well as periodic uploads and downloads of information. This provides for a primary backup of all the information generated in the electronic boarding pass system 100. Alternatively, some of the devices 206-216 may store data locally”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terada to include the limitation(s) discussed and also taught by Natale, with the aspect discussed above, as the cited prior arts are at least considered to be analogous arts if not also in the same field of endeavor relating to document and/or character recognizing/processing arts. Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terada by the teachings of Natale, and to incorporate the limitation(s) discussed and also taught by Natale, thereby “...the OCR server 170 may employ known algorithms to determine that the image contains an MRZ, ascertain the format of the MRZ, and extract the text in the MRZ from the image. If the OCR server 170 was able to extract the text from the MRZ (block 910), the OCR server 170 ma