CTFR 18/604,347 CTFR 99718 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 amendments and remarks filed on 04/23/2026. Claims 1-16 are pending. Corrective Actions by Applicant Claims 1 and 15-16 have been amended. Response to Arguments The examiner has fully considered Applicant’s presented arguments. On page 7 of the remarks, Applicant argues that the amendments to claims 1 and 15-16 prevent a 35 U.S.C. 112(f) interpretation by reciting sufficient structure, material, or acts for performing the claimed function. The examiner respectfully disagrees. The three-prong analysis is summarized below: Prong (A) Generic Placeholders Prong (B) Linking word(s) + function Prong (C) Structure/material/acts Information Processing Apparatus Not followed by a linking word, but all recited units below that comprise the apparatus invoke 112(f) as well. No other structure, material, or acts further modify the apparatus, outside of the other elements that also invoke 112(f). Obtaining unit Followed by “configured to…” and then functions performed. No other structure, material, or acts outside of functions performed by the respective units. First determination unit Second determination unit Correction unit Update unit Display control unit Display unit Displays a character string (as recited by claim 10). In the context of 112(f), the present amendment to claim 1 only appears to add functional language that modifies the recited “second determination unit”. It is unclear from Applicant’s remarks exactly how the amended claims now avoid a 112(f) interpretation. Further, Applicant’s statement that they do not intend to invoke 112(f) is acknowledged. However, “application of 35 U.S.C. 112(f) is driven by the claim language, not by applicant’s intent or mere statements to the contrary included in the specification or made during prosecution” (see MPEP 2181.I, first paragraph). Accordingly, to avoid a 112(f) interpretation, the examiner suggests amending claim 1 to recite that the information processing apparatus also comprises a processor that implements all of the recited functional units (see paragraph 0059 of the originally-filed specification for support). Alternatively, claims 1-14 could be rewritten to remove the units entirely, and instead have a processor perform all of the recited functions. On pages 7-11 of the remarks, Applicant argues that the amended claims are all directed to patent-eligible subject matter under 35 U.S.C. 101. The examiner respectfully disagrees. For brevity, see updated 101 rejections below with additional comments. On pages 11-13 of the remarks, Applicant argues that the amended claims overcome all previous 35 U.S.C. 103 rejections. The examiner respectfully disagrees. As illustrated in the updated 103 rejections below, the amended passage of the independent claims is still disclosed by Rubio, and applicant’s arguments do not appear to give any specific details or reasoning to the contrary beyond conclusory statements. Claim Rejections – 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding claim 2, attention is first drawn to the present claim 1, on which claim 2 depends. Claim 1 recites “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 ” (emphasis added). Thus, the trained model is interpreted to output the character strings corresponding to the first item. Claim 2 then further recites “wherein the second determination unit determines the character string corresponding to the second item from among the character strings corresponding to the first item ” (emphasis added). Thus, if the second character strings are chosen from among the first character strings, then the second character strings must also have been previously output by the trained model. However, claim 1 as amended now recites “a second determination unit configured to determine a character string corresponding to a second item that is not output by the trained model ” (emphasis added). Thus, it is unclear how the second character strings can now be selected from among the first character strings if the second character strings cannot be outputs of the trained model. Claim Rejections – 35 USC § 101 07-04-01 AIA 07-04 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 that is not output by the trained model, by applying both the document type and the character strings corresponding to the first item to a rule-based algorithm that derives the character string corresponding to the second item based on a relationship between the document type and the character strings corresponding to the first item. 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 are regarded as reciting mental processes, such as observations, evaluations, judgements, or opinions, that can be practically performed in the human mind. Part c requires determining both a document type represented by the image, as well as character strings corresponding to a first item in the image. Such a determination can be fulfilled by a mental analysis or recognition by observing such an image. Notably, the determinations are performed “by using a result obtained by inputting the token string into a trained model”; thus, a human simply recognizing the output of a trained model also reads on this limitation. Part d requires determining character strings corresponding to a second item not output by the trained model. This is done by applying the document type and first character strings to a rule-based algorithm. This can be any algorithm, as long as it considers a relationship between the document type and first character strings. Thus, any mental analysis that determines the second character strings would read on the claim, as long as it considers the relationship between the document type and first character strings, which is certainly mentally performable. Finally, 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). Even though the claims recite that the above processes are performed on a computer, said processes are still broad enough such that they can be practically performed in the human mind as well (YES). Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? Parts a and b, as well as the first/second determination units of parts c/d, respectively, are regarded as additional elements in the claim. Part a and each of the functional units are regarded as a computerized system recited at a high level of generality that is only used, in the scope of the claim, to perform the mental processes. The functional units are not regarded as a particular architecture, as in the scope of the claim they merely describe what the generalized system functionally accomplishes. Additionally, the obtaining unit of part b does obtain a token string generated based on the character strings in the image, but in the scope of the claim these token strings are only used to perform further mental processes on the system (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, divided into functional units. The functional units first obtain a token string from characters in an image, which is a well-understood, routine, conventional activity in the field of image processing. The remaining functional units then use the token strings to exclusively perform processes that are also practically performable in the human mind. (NO). Claim 1 is not eligible. Similar analysis is applicable to independent claims 15 and 16. Claim 15 instead recites a method without a computerized system. Claim 16 instead recites a non-transitory computer readable storage medium storing a program which causes a computer to perform an information processing method, which is still regarded as a generalized computerized system. 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; note part c of claim 1 above, which includes a mental analysis of the output of a trained model, regardless of the details of said model. 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 07-20-aia AIA 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. 07-21-aia AIA Claim s 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 a document type represented by the document image (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 that is not output by the trained model (see figure 5, step 512 and column 14, lines 20-28, where once the final prior and post blocks are identified, the tokens between the prior and post blocks are then finally extracted as a second item; note that Rubio's trained model only outputs the prior and post blocks before this final extraction step ) , by applying both the document type and the character strings corresponding to the first item to a rule-based algorithm (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) that derives the character string corresponding to the second item based on a relationship between the document type and the character strings corresponding to the first item (figures 6A-6B and column 14, line 41 to column 15, line 67 provide details of this iterative algorithm for determining the prior and post blocks – any iteration of this algorithm can be considered to use the relationship between the prior & post blocks/first character strings and the extraction field template/document type to ultimately derive the second item above). Rubio fails to disclose determining a document type represented by the document image by inputting the token string into a trained model. More specifically, Rubio does generally determine a document type for use in further processing, just not via the claimed method above. 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 a document type represented by a document image and items corresponding to character strings included in the document image (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 . 07-21-aia AIA 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) . 07-21-aia AIA Claim s 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 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Contact Information 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. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /NICHOLAS JOHN HELCO/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667 Application/Control Number: 18/604,347 Page 2 Art Unit: 2667 Application/Control Number: 18/604,347 Page 3 Art Unit: 2667 Application/Control Number: 18/604,347 Page 6 Art Unit: 2667 Application/Control Number: 18/604,347 Page 7 Art Unit: 2667 Application/Control Number: 18/604,347 Page 8 Art Unit: 2667 Application/Control Number: 18/604,347 Page 9 Art Unit: 2667 Application/Control Number: 18/604,347 Page 11 Art Unit: 2667 Application/Control Number: 18/604,347 Page 12 Art Unit: 2667 Application/Control Number: 18/604,347 Page 13 Art Unit: 2667 Application/Control Number: 18/604,347 Page 14 Art Unit: 2667 Application/Control Number: 18/604,347 Page 15 Art Unit: 2667 Application/Control Number: 18/604,347 Page 16 Art Unit: 2667 Application/Control Number: 18/604,347 Page 17 Art Unit: 2667 Application/Control Number: 18/604,347 Page 19 Art Unit: 2667 Application/Control Number: 18/604,347 Page 20 Art Unit: 2667 Application/Control Number: 18/604,347 Page 21 Art Unit: 2667 Application/Control Number: 18/604,347 Page 22 Art Unit: 2667 Application/Control Number: 18/604,347 Page 23 Art Unit: 2667 Application/Control Number: 18/604,347 Page 24 Art Unit: 2667 Application/Control Number: 18/604,347 Page 25 Art Unit: 2667 Application/Control Number: 18/604,347 Page 26 Art Unit: 2667
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