DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s Amendment and remarks dated 3/3/2026 have been considered. Claims 1-20 are pending.
Response to Arguments
On page 7 of Applicant’s 3/3/2026 Amendment and remarks, Applicant asserts that no new matter has been introduced by the amendments to independent claims 1, 8, and 15, and identifies paras. 24-27 and 36 as providing sufficient written description support.
The examiner agrees that the portions of the disclosure identified by Applicant provide sufficient written description support for the claim amendments.
On page 8 of Applicant’s 3/3/2026 Amendment and remarks, with respect to the rejections of all claims under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant argues:
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The examiner respectfully disagrees. Applicant acknowledges that steps can be done in the human mind, but simply argues that it is not “logical nor practical to do it in the mind.” However, as shown in the detailed rejection, for simple examples like parsing a phone number, a human could easily separate a string into delimiters and text strings.
The examiner agrees that the “sending the first token....” limitation is not a mental step. That limitation is addressed under Step 2A, prong 2 and Step 2B.
On page 8 of Applicant’s 3/3/2026 Amendment and remarks, with respect to the rejections of all claims under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant argues:
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The examiner respectfully disagrees. While Applicant alleges that the claims are “directed to solving the problem involving current natural language processing (NLP) techniques being unable to recognize delimiters that could be part of sensitive data” and is therefore “an improvement to computer technology,” the examiner notes that such improvement is asserted in a “conclusory manner” such that one of ordinary skill would not understand that the claimed invention improves any technology. See MPEP 2106.04(d)(1). Further, the NLP techniques allegedly recited are merely mental processes as explained in the detailed rejection, so any alleged improvement is to the judicial exception itself and not to any technology or technical field.
On page 8 of Applicant’s 3/3/2026 Amendment and remarks, with respect to the rejections of all claims under 35 U.S.C. 101, with respect to Step 2B, Applicant argues:
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The examiner respectfully disagrees. MPEP 2106.05(d) explains that the “well-understood, routine, and conventional” test is an indicator, but not a standalone test, for eligibility. Further, Applicant provides no evidence establishing that such string manipulation techniques are not “well-understood, routine, and conventional.” The examiner provides a citation to “Append() Method in Java: StringBuilder and StringBuffer”, available at web.archive.org/web/20201028111033/https://codegym.cc/groups/posts/append-method-in-java (archived 10/28/2020), which explains that append() is a known Java command for the StringBuilder class, and one of ordinary skill would understand that such simple string manipulation operations are known.
On page 9 of Applicant’s 3/3/2026 Amendment and remarks, with respect to the rejection of claims 1, 8, and 15 under 35 U.S.C. 103, Applicant argues with respect to the CODEN reference:
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The examiner respectfully disagrees. CODEN, para. 0083 explicitly states: “Hence, in box 365 it is checked whether the first character in E1 is a period. If that assertion is true, the period is appended in box 370 and removed from the beginning of E1.”
CODEN therefore specifically teaches removing a period from the beginning of string E1. CODEN further teaches appending the period, and one of ordinary skill would understand that such appending would be to the end, and not to the beginning. For example, appending an appendix to a publication adds the appendix to the end of the publication and not to the beginning. CODEN clearly teaches both the concept of removing a delimiter from the beginning of appending the delimiter to the end of a string, and one of ordinary skill in the art would understand how to apply such teachings in the software arts.
On pages 9-10 of Applicant’s 3/3/2026 Amendment and remarks, with respect to the rejection of all dependent claims under 35 U.S.C. 103, Applicant argues that such dependent claims should be allowed for the same reasons argued with respect to claim 1.
The examiner respectfully disagrees for the same reasons explained with respect to claim 1.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-7 are directed to a method (a process), Claims 8-14 are directed to a non-transitory computer readable medium (an article of manufacture), and Claims 15-20 are directed to a system (a machine), which each fall within one of the four statutory categories of inventions.
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “computer implemented”, “computing devices”, “trained model”).
identifying ... a delimiter based on the text string; (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, identify a delimiter in a text string, such as the “-“ delimiter in “The phone number for the USPTO is 1-800-786-9199” string)
based on identifying the delimiter, identifying, ... to a predetermined length of characters, a first character sequence to the left of the delimiter and a second character sequence to the right of the delimiter; (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, identify predetermined length of characters, such as 3 digits to the left of the 3rd “-“ delimiter in “The phone number for the USPTO is 1-800-786-9199” string (786) and 4 digits to the right of the 3rd “-“ delimiter of the same string (9199))
determining, ... whether the first character sequence or the second character sequence indicates the delimiter is part of a continuous string of text; and (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, determine that a delimiter is part of a greater string, such as the entire phone number in “The phone number for the USPTO is 1-800-786-9199” string, where the first character sequence is “786” and the second character sequence is “9199”)
based on determining that the delimiter is part of the continuous string of text (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, determine that a delimiter is part of a greater string, and can use that information to mentally perform subsequent steps)
generating, ..., a first token representing the continuous string of text by removing the delimiter from the continuous string of text and appending the delimiter to the end of the continuous string of text; and (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, determine that a delimiter is part of a greater string, and can then generate a token by writing a token on a piece of paper, such as by writing the phone number in “The phone number for the USPTO is 1-800-786-9199” string on a paper token by manipulating the string to read “The phone number for the USPTO is 1-800-7869199-”).
masking or encrypting, ..., at least one character of the continuous string of text (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, determine that a delimiter is part of a greater string, and can then mask or obfuscate at least one character, such as if the USPTO number was considered a sensitive internal number, it could be written as 1-800-786-xxxx or 1-800-xxx-xxxx to mask the underlying sensitive information)
generating, ... based on determining that the delimiter is not part of the continuous string of text, a second token representing the delimiter. (under the broadest reasonable interpretation, a human can mentally, or with pen and paper, generate a token by writing a token on a piece of paper, such as by writing “ “ (blank space delimiter) in “ The phone number for the USPTO is 1-800-786-9199” string on a paper token).
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “computer implemented”, “computing devices”, “trained model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receiving, by one or more computing devices, a text string” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g))).
Regarding the “by the one or more computing devices” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (See MPEP 2106.05(f)).
Regarding the “using a trained model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (See MPEP 2106.05(f)).
Regarding the “sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified”, limitation, such additional element of a data transmitting step is recited at a high level of generality and amounts to extra-solution activity of transmitting data, i.e. post-solution activity of transmitting data from the claimed process (see MPEP 2106.05(g)). Moreover, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “computer implemented”, “computing devices”, “trained model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receiving, by one or more computing devices, a text string” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “by the one or more computing devices” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “using a trained model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified”, limitation, as discussed above, the additional element of a data transmitting step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. post-solution activity of transmitting data from the claimed process. The courts have found limitations directed to transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Moreover, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”)
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 2
Regarding the “receiving, by the one or more computing devices, the text string in real-time based on inputs entered into a client device” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g))).
Step 2B
Regarding the “receiving, by the one or more computing devices, the text string in real-time based on inputs entered into a client device” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 3
Step 2A, Prong 2
Regarding the “receiving, by the one or more computing devices, a document” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g))).
Regarding the “wherein the text string is embedded in the document” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B
Regarding the “receiving, by the one or more computing devices, a document” limitation, as discussed above, the additional elements of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “wherein the text string is embedded in the document” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which is not significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 4
Step 2A, Prong 2
Regarding the “wherein the trained model is a character-level sequence-to-sequence model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the trained model is a character-level sequence-to-sequence model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which is not significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 5
Step 2A, Prong 2
Regarding the “wherein the trained model is a long short term memory (LSTM) model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the trained model is a long short term memory (LSTM) model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which is not significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 6
Step 2A, Prong 2
Regarding the “wherein the trained model is a recurrent neural network (RNN) model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the trained model is a recurrent neural network (RNN) model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which is not significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 7
Step 2A, Prong
wherein the delimiters comprise: a dash, a semicolon, an underscore, a comma, or a period (under the broadest reasonable interpretation, a human can mentally review a textual string for delimiters that can be one of a dash, semicolon, underscore, comma, or period)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 8
Step 2A, Prong 1
Claim 8 claims a non-transitory computer readable medium that corresponds to the method of claim 1. Therefore, the same analysis with respect to the recited abstract ideas in claim 1 applies to this claim 8.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “non-transitory computer readable medium”, “computing devices”, “processor”, “trained model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 8 claims a non-transitory computer readable medium that corresponds to the method of claim 1. Therefore, the same analysis with respect to Step 2A, prong 2 with respect to claim 1 applies to this claim 8.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “non-transitory computer readable medium”, “computing devices”, “processor”, “trained model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 8 claims a non-transitory computer readable medium that corresponds to the method of claim 1. Therefore, the same analysis with respect to Step 2B with respect to claim 1 applies to this claim 8.
Regarding Claims 9-14
Claims 9-14 depend from claim 8 and correspond to the methods of claims 2-7, respectively. Therefore, the analysis above with respect to claims 2-7, respectively, together with claim 8, applies to each of claims 9-14.
Regarding Claim 15
Step 2A, Prong 1
Claim 15 claims a system that corresponds to the method of claim 1. Therefore, the same analysis with respect to the recited abstract ideas in claim 1 applies to this claim 15.
Step 2A, Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “computing system”, “communications unit”, “control unit”, “trained model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 15 claims a non-transitory computer readable medium that corresponds to the method of claim 1. Therefore, the same analysis with respect to Step 2A, prong 2 with respect to claim 1 applies to this claim 15.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “computing system”, “communications unit”, “control unit”, “trained model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 15 claims a non-transitory computer readable medium that corresponds to the method of claim 1. Therefore, the same analysis with respect to Step 2B with respect to claim 1 applies to this claim 15.
Regarding Claims 16-20
Claims 16-20 depend from claim 15 and correspond to the methods of claims 2 and 4-7, respectively. Therefore, the analysis above with respect to claims 2 and 4-7, respectively, together with claim 15, applies to each of claims 16-20.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7-10, 14-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230064482 A1, hereinafter referenced as VIKRAMARATNE, further in view of US 20160162467 A1, hereinafter referenced as MUNRO, and further in view of US 20180276393 A1, hereinafter referenced as ALLEN, and further in view of US 20020099744 A1, hereinafter referenced as CODEN, and further in view of US 20210149993 A1, hereinafter referenced as TORRES.
Regarding Claim 1
VIKRAMARATNE teaches:
A computer implemented method for partitioning text, the method comprising: (VIKRAMARATNE, para. 0037: “PII detectors are computer-implemented modules that are configured to calculate the likelihood that a particular portion of content (e.g., portion of a content object) does contain a character string that can be used to determine an entity (e.g., a person) that corresponds to that particular portion of content. For example, the character string “(123) 456-6789” might be a phone number that corresponds to a particular person. As another example, the character string “4567 2345 1234 7890” might be a credit card number that corresponds to a particular credit card that is issued to a particular person.”; Examiner’s Note (EN): the broadest reasonable interpretation of “partitioning text” includes identifying particular entities from text strings)
receiving, by one or more computing devices, a text string; (VIKRAMARATNE, para. 0040: “content object 107 is received at the RegEx-based detector, upon which receipt the received content object is subjected to a scan to find out if any of the rules from within the repository of RegEx rules 175 are a “hit” for this document (i.e., the document does contain a particular character sequence that corresponds to a particular RegEx rule).”; VIKRAMARATNE, para. 0099: “FIG. 6A depicts a block diagram of an instance of a computer system 6A00 suitable for implementing embodiments of the present disclosure.”; (EN): the “context object” is a document that contains text strings; see Fig. 3A for an example document)
identifying, by the one or more computing devices, a delimiter based on the text string; (VIKRAMARATNE, para. 0041: “As one example of the foregoing path, it might be that the content object that is considered in the RegEx-based detector contains a phone number (e.g., “(123) 456-7890”, and that phone number matches the formatting as specified in a RegEx, namely “(?([1-9]{3}\).[0-9]{3}[0-9]{4}/”.”;
(EN): the portion of the regular expression - “\).“ - looks for the closed parentheses delimiter based on the text string potentially being a phone number, where the area code is sometimes enclosed in parentheses)
based on identifying the delimiter, identifying, by the one or more computing devices and to a predetermined length of characters, a first character sequence to the left of the delimiter and a second character sequence to the right of the delimiter; (VIKRAMARATNE, para. 0041: “As one example of the foregoing path, it might be that the content object that is considered in the RegEx-based detector contains a phone number (e.g., “(123) 456-7890”, and that phone number matches the formatting as specified in a RegEx, namely “(^([1-9]{3}\).[0-9]{3}[0-9]{4}/”.”; (EN): the portion of the regular expression - “[1-9]{3}“ - looks for 3 consecutive digits [1-9] to the left of the closed parentheses delimiter, corresponding to the “first character sequence” and the remainder of the regular expression looks for 3 digits and 4 digits for the 7-digit telephone number)
determining, by the one or more computing devices and using a trained model, whether the first character sequence or the second character sequence indicates the delimiter is part of a continuous string of text; and (VIKRAMARATNE, para. 0066: “In cases when the RegEx detector invokes machine learning detector 132, the machine learning detector will select input words from a portion of the content object that is proximal or otherwise related (e.g., via a link) to the location in the content object where the hit occurred (step 166). Those input words are provided as input signals to a machine learning classifier (step 174) and the classifier will emit a label (e.g., ML label). The ML label might be the same label as the given RegEx rule label, or it might be different. For example, the RegEx label might be “Phone Number”, whereas the ML label might be “Mobile Phone Number””; (EN): machine learning detector 132 corresponds to the “trained model” which will classify the string, such as the string “(123) 456-7890” being classified as a phone number, where the closed parentheses delimiter is part of the phone number as determined by the phone number expression)
However, VIKRAMARATNE fails to explicitly teach:
based on determining that the delimiter is part of the continuous string of text:
generating by the one or more computing devices, a first token representing the continuous string of text by removing the delimiter from the continuous string of text and appending the delimiter to the end of the continuous string of text; and
masking or encrypting, by the one or more computing devices, at least one character of the continuous string of text
generating, by the one or more computing devices and based on determining that the delimiter is not part of the continuous string of text, a second token representing the delimiter
sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified
However, in a related field of endeavor (machine learning with respect to natural language models, see para. 0003), MUNRO teaches:
based on determining that the delimiter is part of the continuous string of text: generating by the one or more computing devices, a first token representing the continuous string of text; (MUNRO, para. 0051: “At step 520, the text of the document may be partitioned into a plurality of tokens, which are strings organized in a consistent manner (e.g., the document is subdivided into an array of tokens, such as single words or parts thereof, spaces, punctuation, substrings of words that have meaningful internal boundaries, and groups of words, in the order they appear) by a tokenizer program or engine.”;
(EN): in combination with VIKRAMARATNE, the system of VIKRAMARATNE is modified to create tokens from the string of text, where a continuous substring having a delimiter such as a formatted phone number can be a single token as in MUNRO, and where such determination is based on the delimiter (e.g., right parentheses) being part of a formatted phone number string)
generating, by the one or more computing devices and based on determining that the delimiter is not part of the continuous string of text, a second token representing the delimiter. (MUNRO, para. 0051: “At step 520, the text of the document may be partitioned into a plurality of tokens, which are strings organized in a consistent manner (e.g., the document is subdivided into an array of tokens, such as single words or parts thereof, spaces, punctuation, substrings of words that have meaningful internal boundaries, and groups of words, in the order they appear) by a tokenizer program or engine.”;
(EN): in combination with VIKRAMARATNE, the system of VIKRAMARATNE is modified to create tokens from the string of text, where a token can be a single punctuation symbol as in MUNRO if the symbol is not part of a greater substring in the string, such as a period at the end of a phone number that completes a sentence.)
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE with the teachings of MUNRO as explained above. As disclosed by MUNRO, one of ordinary skill would have been motivated to do so because MUNRO teaches that tokenizing a text document in a “common format” will result in “strings organized in a consistent manner”. (para. 0051).
However, VIKRAMARATNE and MUNRO fail to explicitly teach:
... by removing the delimiter from the continuous string of text and appending the delimiter to the end of the continuous string of text
masking or encrypting, by the one or more computing devices, at least one character of the continuous string of text
sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified
However, in a related field of endeavor (information protection and management techniques for documents that include sensitive data, see para. 0002), ALLEN teaches:
masking or encrypting, by the one or more computing devices, at least one character of the continuous string of text (ALLEN, para. 0039: “In FIG. 2, a first obfuscation option includes replacing the sensitive content with masked or otherwise obfuscated text, while a second obfuscation option includes replacing all content with a pattern or data scheme similar to the content of the currently selected annotation. For example, if a SSN is included in a cell, a user might be presented with options to replace the digits in the SSN with ‘X’ characters while leaving intact a data scheme of the SSN, i.e. leaving in the familiar “3-2-4” character arrangement separated by dash characters. Moreover, a further obfuscation option can include an option to replace all of the SSNs that fit the pattern of the selected SSN with ‘X’ characters. It should be understood that different example obfuscation options can be presented, and different characters can be used in the replacement process. However, regardless of the obfuscation characters employed, the sensitive data is rendered anonymized, sanitized, ‘clean,’ or unidentifiable as the original content.”;
(EN): as shown in the Fig. 2 example, based on the data pattern of a SSN (xxx-xx-xxxx), where the SSN has “-“ delimiters, masking the SSN consistent with such data pattern; in combination with VIKRAMARATNE and MUNRO, the system of VIKRAMARATNE is modified to utilize the data patterns of ALLEN in order to mask, or obfuscate sensitive data consistent with such data pattern (and associated delimiters) as disclosed by ALLEN.)
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE, MUNRO, and ALLEN as disclosed above. As disclosed by ALLEN, one of ordinary skill would have been motivated to do so because ALLEN teaches that information protection and management techniques attempt to avoid “misappropriation and misallocation of ... sensitive data” in documents such as social security numbers and credit card information. (paras. 0001-0002).
However, VIKRAMARATNE, MUNRO, and ALLEN fail to explicitly teach:
by removing the delimiter from the continuous string of text and appending the delimiter to the end of the continuous string of text
sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified
However, in a related field of endeavor (text processing, see para. 0003), CODEN teaches:
by removing the delimiter from the continuous string of text and appending the delimiter to the end of the continuous string of text (CODEN, para. 0041: “where a word is any token separated by white space containing at least one letter.”;
CODEN, para. 0043: “Second, if the word matches the regular expression "[a-z].backslash.. {2, }" (single letter followed by a period, repeated two or more times), the capitalization recovery system 10 assumes the word is an abbreviation (acronym)”;
CODEN, para. 0083: “Hence, in box 365 it is checked whether the first character in E1 is a period. If that assertion is true, the period is appended in box 370 and removed from the beginning of E1.”;
Examiner’s Note: CODEN teaches removing a period (e.g., a type of delimiter) from a string represented by tokens and appending the period to the end of the string; the VIKRAMARATNE-MUNRO-ALLEN-CODEN combination now modifies the system of VIKRAMARATNE to utilize regular expressions (as in both CODEN and VIKRAMARATNE (see para. 0027)) to remove a delimiter such as a period from a string and append such delimiter to the end of the string as taught by CODEN, which would be straightforward to implement using regular expressions)
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE, MUNRO, ALLEN, and CODEN as explained above. As disclosed by CODEN, one of ordinary skill would have been motivated to use regular expressions (which are also taught by VIKRAMARATNE, see para. 0027) to manipulate the punctuation of a string, including moving a period from the beginning to the end of a string as taught by CODEN. (paras. 0043, 0083). One of ordinary skill would further be motivated to perform such a string manipulation, such as to change the way a datestamp is represented, for example.
However, VIKRAMARATNE, MUNRO, ALLEN and CODEN fail to explicitly teach:
sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified
However, in a related field of endeavor (natural language processing, see para. 0014), TORRES teaches and makes obvious:
sending, by the one or more computing devices, the first token or the second token to further downstream components to be classified (TORRES, para. 0045: “In some example, the output corresponding to the first position (e.g., position of the [CLS] token) may be input to a downstream task (e.g., classifier) for the corresponding task (e.g., classification, etc.).”;
Examiner’s Note: CODEN teaches removing a period (e.g., a type of delimiter) from a string represented by tokens and appending the period to the end of the string; the VIKRAMARATNE-MUNRO-ALLEN-CODEN-TORRES combination now modifies the system of VIKRAMARATNE to have a downstream classifier that can classify a token as in TORRES)
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES as explained above. As disclosed by TORRES, one of ordinary skill would have been motivated to so in order to separately optimize “different downstream tasks.” (para. 0048). One of ordinary skill would understand the benefit of having a special module for a classification task, such as an open-source text classifier, and would be motivated to send the classification task to a known and proven classifier downstream.
Regarding Claim 2
VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES teach the method of claim 1 as explained above. VIKRAMARATNE further teaches:
receiving, by the one or more computing devices, the text string in real-time based on inputs entered into a client device. (VIKRAMARATNE, para. 0037: “Specifically, the figure shows pathways from original provision of content objects through a labeling module of the content management so as to produce labeled content objects, which can at that time or at a later time, can be handled in accordance with any then-current governance requirements”; VIKRAMARATNE, para. 0062: “As used herein, a content management system is a computing system comprising executable code that facilitates performance of a set of coordinated functions, tasks, or activities on behalf of a plurality of collaborating users that operate over shared content objects. More specifically, a content management system facilitates collaboration activities such as creating a shared content object, establishing a set of users who can access the shared content object, concurrently (e.g., by multiple users at the same time) viewing a shared content object, concurrently editing a shared content object, modifying sharing configurations that pertain to a shared content object, and so on.”; (EN): Fig. 1A shows that user devices 105 provide user-provided content 106 into the content management system 104, where processing can be performed “at that time” corresponding to recited “real-time” limitation)
Regarding Claim 3
VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES teach the method of claim 1 as explained above. VIKRAMARATNE further teaches:
receiving, by the one or more computing devices, a document; and (VIKRAMARATNE, para. 0040: “content object 107 is received at the RegEx-based detector, upon which receipt the received content object is subjected to a scan to find out if any of the rules from within the repository of RegEx rules 175 are a “hit” for this document (i.e., the document does contain a particular character sequence that corresponds to a particular RegEx rule).”; VIKRAMARATNE, para. 0099: “FIG. 6A depicts a block diagram of an instance of a computer system 6A00 suitable for implementing embodiments of the present disclosure.”; (EN): the “context object” is a document that contains text strings)
wherein the text string is embedded in the document. (VIKRAMARATNE, para. 0027: “The additional context information might be garnered from portions of the document that appear before or after the candidate string. In this case, if the document under consideration were a transcription of a text exchange between two participants, then the candidate question “Is (123) 456-7890 your mobile?””; (EN): a document comprises one or more text strings, such as in the example “(123) 456-7890” and “your mobile” are each text strings that are part of a greater text string)
Regarding Claim 7
VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES teach the method of claim 1 as explained above. VIKRAMARATNE further teaches:
wherein the delimiters comprise: a dash, a semicolon, an underscore, a comma, or a period. (VIKRAMARATNE, para. 0041: “As one example of the foregoing path, it might be that the content object that is considered in the RegEx-based detector contains a phone number (e.g., “(123) 456-7890”, and that phone number matches the formatting as specified in a RegEx, namely “(?([1-9]{3}\).[0-9]{3}[0-9]{4}/”.”; (EN): the “-“ delimiter in the phone number is a dash)
Regarding Claim 8
VIKRAMARATNE teaches:
A non-transitory computer readable medium (VIKRAMARATNE, para. 0100: “ computer system 6A00 performs specific operations by data processor 607 executing one or more sequences of one or more program instructions contained in a memory. Such instructions (e.g., program instructions 6021, program instructions 6022, program instructions 6023, etc.) can be contained in or can be read into a storage location or memory from any computer readable/usable storage medium such as a static storage device or a disk drive.”) including instructions for partitioning text that when executed by a processor perform the operations comprising: (VIKRAMARATNE, para. 0037: “PII detectors are computer-implemented modules that are configured to calculate the likelihood that a particular portion of content (e.g., portion of a content object) does contain a character string that can be used to determine an entity (e.g., a person) that corresponds to that particular portion of content. For example, the character string “(123) 456-6789” might be a phone number that corresponds to a particular person. As another example, the character string “4567 2345 1234 7890” might be a credit card number that corresponds to a particular credit card that is issued to a particular person.”; Examiner’s Note (EN): the broadest reasonable interpretation of “partitioning text” includes identifying particular entities from text strings)
The remaining limitations in claim 8 correspond to the method of claim 1, and therefore claim 8 is rejected for the same reasons explained above with respect to claim 1.
Claim 9 depends from claim 8 and claims a non-transitory computer readable medium corresponding to the method of claim 2, and therefore is rejected for the same reasons explained above with respect to claims 2 and 8.
Claim 10 depends from claim 8 and claims a non-transitory computer readable medium corresponding to the method of claim 3, and therefore is rejected for the same reasons explained above with respect to claims 3 and 8.
Claim 14 depends from claim 8 and claims a non-transitory computer readable medium corresponding to the method of claim 7, and therefore is rejected for the same reasons explained above with respect to claims 7 and 8.
Regarding Claim 15
VIKRAMARATNE teaches:
A computing system for partitioning text comprising: (VIKRAMARATNE, para. 0037: “PII detectors are computer-implemented modules that are configured to calculate the likelihood that a particular portion of content (e.g., portion of a content object) does contain a character string that can be used to determine an entity (e.g., a person) that corresponds to that particular portion of content. For example, the character string “(123) 456-6789” might be a phone number that corresponds to a particular person. As another example, the character string “4567 2345 1234 7890” might be a credit card number that corresponds to a particular credit card that is issued to a particular person.”; Examiner’s Note (EN): the broadest reasonable interpretation of “partitioning text” includes identifying particular entities from text strings)
a communications unit configured to receive a text string; (VIKRAMARATNE, para. 0040: “content object 107 is received at the RegEx-based detector, upon which receipt the received content object is subjected to a scan to find out if any of the rules from within the repository of RegEx rules 175 are a “hit” for this document (i.e., the document does contain a particular character sequence that corresponds to a particular RegEx rule).”; VIKRAMARATNE, para. 0101: “Instances of communications interface 614 may comprise one or more networking ports that are configurable (e.g., pertaining to speed, protocol, physical layer characteristics, media access characteristics, etc.) and any particular instance of communications interface 614 or port thereto can be configured differently from any other particular instance.”)
a control unit, coupled to the communications unit, configured to: (VIKRAMARATNE, para. 0100: “ computer system 6A00 performs specific operations by data processor 607 executing one or more sequences of one or more program instructions contained in a memory. Such instructions (e.g., program instructions 6021, program instructions 6022, program instructions 6023, etc.) can be contained in or can be read into a storage location or memory from any computer readable/usable storage medium such as a static storage device or a disk drive.”)
The remaining limitations in claim 15 correspond to the method of claim 1, and therefore claim 15 is rejected for the same reasons explained above with respect to claim 1.
Claim 16 depends from claim 15 and claims a system corresponding to the method of claim 2, and therefore is rejected for the same reasons explained above with respect to claims 2 and 15.
Claim 20 depends from claim 15 and claims a system corresponding to the method of claim 7 and therefore is rejected for the same reasons explained above with respect to claims 7 and 15.
Claims 4-6, 11-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over VIKRAMARATNE in view of MUNRO and ALLEN and CODEN and TORRES and further in view of US 20210110254 A1, hereinafter referenced as HOANG.
Regarding Claim 4
VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES teach the method of claim 1 as explained above. However, VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES do not explicitly teach:
wherein the trained model is a character-level sequence-to-sequence model.
However, in a related field of endeavor, (training of natural language models, para. 0040), HOANG teaches:
wherein the trained model is a character-level sequence-to-sequence model. (HOANG, para. 0097: “The types of tokens in the input (source) and output (target) sequences can vary according to the application provided by operation of the configured neural seq2seq model, and accordingly the domains of the source and target sequences. For instance, the tokens in the input sequence and/or the target sequence may be of one or more of words, characters (e.g., letters, symbols, etc.), words and characters (e.g., a combination of letters and symbols such as punctuation or numerals) images, or sounds.”; (EN): in combination with VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES, the machine learning detector 132 of VIKRAMARATNE is now implemented as a seq2seq model, where the input and target sequences are on a character level, as disclosed by HOANG).
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE, MUNRO, ALLEN, CODEN, TORRES, and HOANG as explained above. As disclosed by HOANG one of ordinary skill would have been motivated to do so because HOANG teaches techniques for improving neural seq2seq models “by considering both local loss and global loss in generated target sequences” to improve the effectiveness of such models. (para. 0029; see also, para. 0007).
Regarding Claim 5
VIKRAMARATNE, MUNRO, ALLEN, CODEN, TORRES, and HOANG teach the method of claim 4 as explained above. However, VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES do not explicitly teach:
wherein the trained model is a long short term memory (LSTM) model.
However, in a related field of endeavor, (training of natural language models, para. 0040), HOANG teaches:
wherein the trained model is a long short term memory (LSTM) model. (HOANG, para. 0092: “The seq2seq model may be implemented using one or more neural networks such as but not limited to recurrent neural networks (RNNs) (e.g., long short-term memory networks (LSTM) gated recurrent units (GRU)), as will be appreciated by those of ordinary skill in the art.”; (EN): in combination with VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES, the machine learning detector 132 of VIKRAMARATNE is now implemented as an LSTM seq2seq model as disclosed by HOANG).
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE, MUNRO, ALLEN, CODEN, TORRES, and HOANG as explained above. As disclosed by HOANG one of ordinary skill would have been motivated to do so because HOANG teaches techniques for improving neural seq2seq models “by considering both local loss and global loss in generated target sequences” to improve the effectiveness of such models. (para. 0029; see also, para. 0007).
Regarding Claim 6
VIKRAMARATNE, MUNRO, ALLEN, CODEN, TORRES, and HOANG teach the method of claim 4 as explained above. However, VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES do not explicitly teach:
wherein the trained model is a recurrent neural network (RNN) model.
However, in a related field of endeavor, (training of natural language models, para. 0040), HOANG teaches:
wherein the trained model is a recurrent neural network (RNN) model. (HOANG, para. 0092: “The seq2seq model may be implemented using one or more neural networks such as but not limited to recurrent neural networks (RNNs) (e.g., long short-term memory networks (LSTM) gated recurrent units (GRU)), as will be appreciated by those of ordinary skill in the art.”; (EN): in combination with VIKRAMARATNE, MUNRO, ALLEN, CODEN, and TORRES, the machine learning detector 132 of VIKRAMARATNE is now implemented as an RNN seq2seq model as disclosed by HOANG).
Before the effective filing date of the application, one of ordinary skill in the art would have been motivated to combine the document analysis techniques of VIKRAMARATNE, MUNRO, ALLEN, CODEN, TORRES, and HOANG as explained above. As disclosed by HOANG one of ordinary skill would have been motivated to do so because HOANG teaches techniques for improving neural seq2seq models “by considering both local loss and global loss in generated target sequences” to improve the effectiveness of such models. (para. 0029; see also, para. 0007).
Claim 11 depends from claim 8 and claims a non-transitory computer readable medium corresponding to the method of claim 4, and therefore is rejected for the same reasons explained above with respect to claims 4 and 8.
Claim 12 depends from claim 11 and claims a non-transitory computer readable medium corresponding to the method of claim 5, and therefore is rejected for the same reasons explained above with respect to claims 5 and 11.
Claim 13 depends from claim 11 and claims a non-transitory computer readable medium corresponding to the method of claim 6, and therefore is rejected for the same reasons explained above with respect to claims 6 and 11.
Claim 17 depends from claim 15 and claims a system corresponding to the method of claim 4, and therefore is rejected for the same reasons explained above with respect to claims 4 and 15.
Claim 18 depends from claim 17 and claims a system corresponding to the method of claim 5, and therefore is rejected for the same reasons explained above with respect to claims 5 and 17.
Claim 19 depends from claim 17 and claims a system corresponding to the method of claim 6, and therefore is rejected for the same reasons explained above with respect to claims 6 and 17.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210286831 A1 (Girardi). “This is intuitively promising as certain tokens might be low-occurrent compared to others, yet of major importance for the downstream classification task” (para. 0090).
US 20220405472 A1 (Shah). “The method provides the tf-idf matrix of PWST modified token frequency values for further use by downstream statistical analyzers, such as NLP intent classifiers.” (para. 0046).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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