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 .
Notice to Applicants
This action is in response to the Application filed on 03/13/2024.
Claims 1-16 are pending.
Priority
The Application claims priority to JP2023-043940 with filing date 03/20/2023, which is acknowledged.
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 03/13/2024 has been fully considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Information Processing Apparatus”, present in claims 1-14, with corresponding structure found in at least Figure 1, Information Processing Apparatus 130, Figure 3C, Information Processing Apparatus 130, and paragraphs 0027, 0035, 0045, and 0054-0059 of the originally filed specification,
“Obtaining unit”, present in claim 1, with corresponding structure found in at least Figure 1, Information Processing Unit 131, Figure 7, and paragraph 0036 of the originally filed specification,
“First determination unit”, present in claims 1 and 6, with corresponding structure found in at least Figure 1, Information Processing Unit 131, Figure 10, and paragraph 0036 of the originally filed specification,
“Second determination unit”, present in claims 1-3 and 8-12, with corresponding structure found in at least Figure 1, Information Processing Unit 131, Figures 10 13, and 15, and paragraph 0036 of the originally filed specification,
“Correction unit”, present in claims 9 and 11-12, with corresponding structure found in at least Figure 1, Information Processing Unit 131 and paragraphs 0036, 0183, and 0191 of the originally filed specification,
“Update unit”, present in claim 9, with corresponding structure found in at least Figure 1, Information Processing Unit 131 and paragraphs 0036 and 0193-0197 of the originally filed specification,
“Display control unit”, present in claims 10-12, with corresponding structure found in at least Figure 3B, CPU 331, Figure 3C, CPU 361, and paragraphs 0051 and 0057 of the originally filed specification,
“Display unit”, present in claim 10, with corresponding structure found in at least Figure 3A, Display Device 310, Figure 3B, Display Device 337, Figure 3C, Display Device 367, and paragraphs 0048, 0051, and 0057 of the originally filed specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-8, 10, and 13-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
An information processing apparatus comprising:
an obtaining unit configured to obtain a token string generated based on character strings included in a document image;
a first determination unit configured to determine a document type represented by the document image and character strings corresponding to a first item included in the document image by using a result obtained by inputting the token string into a trained model;
and a second determination unit configured to determine a character string corresponding to a second item by applying the document type and the character strings corresponding to the first item to a rule-based algorithm.
Step 1: Does the claim belong to one of the statutory categories? Claim 1 is directed to a machine, which is a statutory category of invention (YES).
Step 2A Prong One: Does the claim recite a judicial exception? Parts c and d can be regarded as reciting abstract ideas including mental processes, such as observations, evaluations, judgements, or opinions.
Part c recites determining the type of document represented by the image and character strings corresponding to a first item in the image, which can be performed in the human mind. These determinations are only limited by generally “inputting the token string into a trained model”; note that the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer (see MPEP 2106.04(a)(2).III).
Part d recites determining a character string corresponding to a second item by applying the document type and first character strings to a rule-based algorithm, which can also be performed in the human mind. Mentally analyzing the document type and character strings via any algorithm to identify other character strings can be performed in the human mind (YES).
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites an information processing apparatus comprising an obtaining unit, first determination unit, and second determination unit, which amount to a computerized system recited at a high level of generality. The claim further recites obtaining a token string based on character strings in the image, which does not integrate the judicial exceptions into a practical application as it is merely performed in the claim for the ultimate purpose of performing the mental processes (NO).
Step 2B: Does the claim as a whole amount to significantly more than the recited exception? The claim as a whole recites a computerized system at a high level of generality that extracts token strings and then performs mental processes using said token strings. The claim only narrows part c by generally reciting that the mental processes are performed by a trained model without any further details (NO). Claim 1 is not eligible.
Similar analysis is applicable to independent claims 15 and 16. Claims 15 and 16 are not eligible.
Claim 2 recites that the character string representing the second item is chosen from among those representing the first item, which can still be practically performed in the human mind. Claim 2 is not eligible.
Claim 3 narrows the character strings to be numeric strings, and narrows the determination of the character string corresponding to the second item to be based on calculations on the numeric strings corresponding to the first item, which can still be practically performed in the human mind. Claim 3 is not eligible.
Claims 4, 5, and 7 generally narrow the trained model to be generated by machine learning to perform the respective tasks, which does not integrate the judicial exceptions into a practical application. Claims 4, 5, and 7 are not eligible.
Claim 6 recites that the document type is generally determined by inputting the token string into the first trained model, which does not integrate the judicial exceptions into a practical application. Claim 6 further recites that the character strings corresponding to the first item are selected from among items obtained by inputting the token string into the second trained model, which can be practically performed in the human mind. Claim 6 is not eligible.
Claim 8 narrows the rule-based algorithm to include a general determination condition that at least one of the document type and the character strings corresponding to the first item are applied to, which can still be practically performed in the human mind. Claim 8 is not eligible.
Claim 9 recites correcting the character string determined by the second determination unit to a character string designated by a user, and further updating information on the determination condition based on a content of the user correction, both of which cannot be performed in the human mind and integrate the judicial exceptions into a practical application. Claim 9 is eligible.
Claim 10 recites mere data output. Claim 10 is not eligible.
Claims 11 and 12 further recite determining candidate character strings, which can be practically performed in the human mind. Claims 11 and 12 further recite displaying the candidate strings, which amounts to mere data output. However, claims 11 and 12 also further recite correcting the character strings corresponding to the second item to be the candidate strings based on a user selection, which cannot be performed in the human mind and integrates the judicial exceptions into a practical application. Claims 11 and 12 are eligible.
Claims 13 and 14 narrow the items and document types to specific species, which does not integrate the judicial exceptions into a practical application. Claims 13 and 14 are not eligible.
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.
Claims 1-2, 4-8, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Rubio et al. (U.S. Patent US-8620079-B1) in view of Salahi (U.S. Publ. US-2021/0357634-A1).
Regarding claim 1, Rubio discloses an information processing apparatus (see figure 21, computing module 2100, processor 2104, memory 2108, and column 21, line 60 to column 22, line 41) comprising:
an obtaining unit configured to obtain a token string generated based on character strings included in a document image (see column 6, lines 39-63, where an input document is tokenized to identify words, symbols, characters, or character strings appearing in the document; see figure 5, step 503 and column 12, line 54 to column 13, line 3, where the input document is obtained);
a first determination unit configured to determine (although Rubio discloses that the document type/classification can be automatically determined, Rubio only discloses doing so using a general "system or method" in column 8, lines 45-56, not specifically doing so by using a result obtained by inputting the token string into a trained model) and character strings corresponding to a first item included in the document image (column 2, lines 38-61 defines "prior blocks" and "post blocks" to be blocks of tokens that come immediately before and after tokens of interest to be examined; the examiner regards the prior blocks, post blocks, and the tokens between as an instance of a "first item"; figure 5, steps 506-509 and column 13, line 19 to column 14, line 19 provide details of iteratively identifying candidate prior and post blocks, any of which can be designated as a first item) by using a result obtained by inputting the token string into a trained model (column 7, line 55 to column 8, line 5 specifies that a machine learning model is trained to identify prior and post blocks for specific document types);
and a second determination unit configured to determine a character string corresponding to a second item by applying the document type and the character strings corresponding to the first item to a rule-based algorithm (see figure 5, step 512 and column 14, lines 20-28, where tokens between the prior and post blocks are extracted as a second item; column 13, lines 4-18 and column 14, lines 29-40 specify that the final prior and post blocks are partly determined based on the "extraction field template" corresponding to the identified document type; figures 6A-6B and column 14, line 41 to column 15, line 67 provide details of an algorithm for determining the prior and post blocks, and thus the second item between them as well).
Rubio fails to disclose determining a document type represented by the document image by inputting the token string into a trained model.
Pertaining to the same field of endeavor, Salahi discloses determining a document type represented by the document image by inputting the token string into a trained model (see figure 1, step 110 and paragraph 0027, where a document is classified using a trained machine learning model and tokens extracted from the document; figure 2 and paragraph 0028 specify that this includes generating Bag of Words vectors from the tokens, generating topic vectors from the Bag of Words vectors, and classifying the document using the topic vectors).
Rubio and Salahi are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Salahi into Rubio because doing so allows for modifying and further processing of the document according to the classification (see Salahi paragraph 0036).
Regarding claim 2, Rubio in view of Salahi discloses wherein the second determination unit determines the character string corresponding to the second item from among the character strings corresponding to the first item (Rubio column 2, lines 38-61 defines "prior blocks" and "post blocks" to be blocks of tokens that come immediately before and after tokens of interest to be examined; the examiner regards the prior blocks, post blocks, and the tokens between as an instance of a "first item"; see Rubio figure 5, step 512 and column 14, lines 20-28, where tokens between the prior and post blocks are extracted as a second item).
Regarding claim 4, Rubio fails to disclose the limitations of claim 4.
Pertaining to the same field of endeavor, Salahi discloses wherein the trained model includes a first trained model generated by performing machine learning so as to output a document type represented by a document image (see figure 1, step 110 and paragraph 0027, where a document is classified using a trained machine learning model and tokens extracted from the document; figure 2 and paragraph 0028 specify that this includes generating Bag of Words vectors from the tokens, generating topic vectors from the Bag of Words vectors, and classifying the document using the topic vectors).
Rubio and Salahi are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Salahi into Rubio because doing so allows for modifying and further processing of the document according to the classification (see Salahi paragraph 0036).
Regarding claim 5, Rubio in view of Salahi discloses wherein the trained model includes a second trained model generated by performing machine learning so as to output items corresponding to character strings included in a document image (Rubio column 7, line 55 to column 8, line 5 specifies that a machine learning model is trained to identify prior and post blocks for specific document types).
Regarding claim 6, Rubio discloses and determines the character strings corresponding to the first item included in the document image by selecting the first item from among items obtained by inputting the token string into the second trained model (figure 5, steps 506-509 and column 13, line 19 to column 14, line 19 provide details of iteratively identifying candidate prior and post blocks, any of which can be designated as a first item).
Rubio fails to disclose wherein the first determination unit determines the document type represented by the document image by using a result obtained by inputting the token string into the first trained model.
Pertaining to the same field of endeavor, Salahi discloses wherein the first determination unit determines the document type represented by the document image by using a result obtained by inputting the token string into the first trained model (see figure 1, step 110 and paragraph 0027, where a document is classified using a trained machine learning model and tokens extracted from the document; figure 2 and paragraph 0028 specify that this includes generating Bag of Words vectors from the tokens, generating topic vectors from the Bag of Words vectors, and classifying the document using the topic vectors).
Rubio and Salahi are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Salahi into Rubio because doing so allows for modifying and further processing of the document according to the classification (see Salahi paragraph 0036).
Regarding claim 7, Rubio discloses wherein the trained model is a single trained model generated by performing machine learning so as to output (column 7, line 55 to column 8, line 5 specifies that a machine learning model is trained to identify prior and post blocks for specific document types).
Rubio fails to disclose a single trained model generated by performing machine learning so as to output a document type represented by a document image.
Pertaining to the same field of endeavor, Salahi discloses a single trained model generated by performing machine learning so as to output a document type represented by a document image (see figure 1, step 110 and paragraph 0027, where a document is classified using a trained machine learning model and tokens extracted from the document; figure 2 and paragraph 0028 specify that this includes generating Bag of Words vectors from the tokens, generating topic vectors from the Bag of Words vectors, and classifying the document using the topic vectors).
Rubio and Salahi are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Salahi into Rubio because doing so allows for modifying and further processing of the document according to the classification (see Salahi paragraph 0036).
Regarding claim 8, Rubio in view of Salahi discloses wherein the algorithm is an algorithm in which a determination condition for determining the character string corresponding to the second item is set, and the second determination unit determines the character string corresponding to the second item by applying at least one of the document type and the character strings corresponding to the first item to the determination condition (see Rubio figure 6B, step 624 and column 15, lines 24-52, where confidence scores for each prior/post block are calculated; figures 19-20 and column 21, lines 1-23 illustrate example prior/post blocks and their associated confidence values; column 15, lines 53-67 specifies that the final prior and post blocks for determining the second item are selected based on a determination condition of selecting candidate/first item blocks having the highest confidence values).
Regarding claim 13, Rubio in view of Salahi discloses wherein the second item includes an item representing an issuance destination of a document represented by the document image and an item representing an issuance source of the document (see Rubio column 2, lines 38-61, where information associated with the second item can include real estate grantors/issuance sources and real estate grantees/issuance destinations).
Regarding claim 14, Rubio in view of Salahi discloses wherein the document represented by the document image is a document on selling of goods (Rubio column 2, lines 38-61 specifies that input documents can include real estate transactions, as they can include grantors/grantees or mortgagors/mortgagees; figure 16 illustrates part of an example document depicting the purchase of real estate),
and the first item includes a plurality of items including an item indicating a company name of a seller or a name of a person in charge at the seller and an item indicating a company name of a buyer or a name of a person in charge at the buyer (Rubio column 2, lines 38-61 specifies that the prior and post blocks can include names of grantors and grantees).
Regarding claim 15, Rubio discloses an information processing method (see figure 5).
The remainder of claim 15 recites steps identical to those performed by the obtaining unit, first determination unit, and second determination unit of claim 1. Therefore, Rubio in view of Salahi discloses claim 15 as applied to claim 1 above.
Regarding claim 16, Rubio discloses a non-transitory computer readable storage medium storing a program (see figure 21, storage media 2114 and column 22, lines 42-56) which causes a computer to perform an information processing method, the information processing method comprising (see figure 5).
The remainder of claim 16 recites steps identical to those performed by the obtaining unit, first determination unit, and second determination unit of claim 1. Therefore, Rubio in view of Salahi discloses claim 16 as applied to claim 1 above.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Rubio et al. (U.S. Patent US-8620079-B1) in view of Salahi (U.S. Publ. US-2021/0357634-A1), and further in view of Subramanian et al. (U.S. Publ. US-2023/0162830-A1).
Regarding claim 3, Rubio in view of Salahi fails to disclose wherein the character strings corresponding to the first item and the character string corresponding to the second item are numeric strings, and the second determination unit determines a numeric string obtained by performing a calculation with the numeric strings corresponding to the first item based on the algorithm as the numeric string corresponding to the second item.
More specifically, Rubio, in column 12, lines 4-25 states that the tokens within the prior and post blocks can be normalized by replacing them with predetermined token markers, such as "[[NUMBER]]" or "[[DATE]]", and furthermore that said normalizations are taken into account during the extraction process, but Rubio does not explicitly disclose that these markers can be represented as integers. Thus, to disclose claim 3 in combination with Rubio and Salahi, a secondary reference would only need to suggest encoding Rubio’s normalized tokens as integers.
Pertaining to the same field of endeavor, Subramanian discloses wherein the character strings corresponding to the first item and the character string corresponding to the second item are numeric strings, and the second determination unit determines a numeric string obtained by performing a calculation with the numeric strings corresponding to the first item based on the algorithm as the numeric string corresponding to the second item (see paragraph 0161, where each token in the document can be replaced with an integer value corresponding to its determined type).
Rubio and Subramanian are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Subramanian into Rubio and Salahi because doing so allows for vectorizing the data for the model to more easily understand (see Subramanian paragraph 0162).
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Rubio et al. (U.S. Patent US-8620079-B1) in view of Salahi (U.S. Publ. US-2021/0357634-A1), and further in view of Jensen (U.S. Publ. US-2011/0096983-A1).
Regarding claim 9, Rubio in view of Salahi fails to disclose the limitations of claim 9.
Pertaining to the same field of endeavor, Jensen discloses a correction unit configured to correct the character string determined by the second determination unit to a character string designated by a user (see figure 5, step 560 and paragraphs 0057-0058, where the user can select the correct text identification from among multiple candidates, or manually enter the correct text from the document; see figure 5, step 580 and paragraph 0060, where the final corrected text is stored);
and an update unit configured to update information on the determination condition based on a content of the correction by the user (see figure 5, step 570 and paragraph 0058, where the user selection can be used as learning information).
Rubio and Jensen are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Jensen into Rubio and Salahi because doing so improves learning for future text detection (see Jensen paragraphs 0058-0059).
Regarding claim 10, Rubio in view of Salahi fails to disclose the limitations of claim 10.
Pertaining to the same field of endeavor, Jensen discloses a display control unit configured to display the character string determined by the second determination unit on a display unit (see figure 3, Display 332, figure 5, step 550, and paragraph 0057, where the candidate text identifications are presented to a user).
Rubio and Jensen are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Jensen into Rubio and Salahi because doing so allows for the user to correct the text identification if it is incorrect (see Jensen paragraph 0058).
Regarding claim 11, Rubio in view of Salahi discloses wherein the second determination unit further determines a candidate character string other than the character string corresponding to the second item (see Rubio figure 6B, step 624 and column 15, lines 24-52, where confidence scores for each prior/post block, and thus the second item between them, are calculated; figures 19-20 and column 21, lines 1-23 illustrate example prior/post blocks and their associated confidence values).
Rubio in view of Salahi fails to disclose the remainder of claim 11.
Pertaining to the same field of endeavor, Jensen discloses the display control unit further displays the candidate character string (see figure 5, step 550 and paragraph 0057, where the candidate text identifications are presented to a user),
and the information processing apparatus further comprises a correction unit configured to make a correction in a case where a user selects the candidate character string such that the candidate character string becomes the character string corresponding to the second item (see figure 5, step 560 and paragraphs 0057-0058, where the user can select the correct text identification from among multiple candidates, or manually enter the correct text from the document; see figure 5, step 580 and paragraph 0060, where the final corrected text is stored).
Rubio and Jensen are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Jensen into Rubio and Salahi because doing so allows for the user to correct the text identification if it is incorrect (see Jensen paragraph 0058).
Regarding claim 12, Rubio in view of Salahi discloses wherein the second item includes a plurality of items (Rubio column 14, lines 20-28 specifies that there can be a plurality of tokens extracted from between the prior and post blocks),
the second determination unit further determines candidate character strings other than the character strings corresponding to the plurality of items (see Rubio figure 6B, step 624 and column 15, lines 24-52, where confidence scores for each prior/post block, and thus the second item between them, are calculated; figures 19-20 and column 21, lines 1-23 illustrate example prior/post blocks and their associated confidence values).
Rubio in view of Salahi fails to disclose the remainder of claim 12.
Pertaining to the same field of endeavor, Jensen discloses the display control unit further displays the candidate character strings corresponding to the plurality of items (see figure 5, step 550 and paragraph 0057, where the candidate text identifications are presented to a user),
and the information processing apparatus further comprises a correction unit configured to correct the character strings corresponding to the plurality of items by using the candidate character strings corresponding to the plurality of items in a case where a user selects the candidate character string corresponding to one of the plurality of items (see figure 5, step 560 and paragraphs 0057-0058, where the user can select the correct text identification from among multiple candidates, or manually enter the correct text from the document; see figure 5, step 580 and paragraph 0060, where the final corrected text is stored).
Rubio and Jensen are considered analogous art, as they are both directed to image processing and machine learning for document tokenization and analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Jensen into Rubio and Salahi because doing so allows for the user to correct the text identification if it is incorrect (see Jensen paragraph 0058).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS JOHN HELCO whose telephone number is (703)756-5539. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached at telephone number 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS JOHN HELCO/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667