DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been received.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/10/2025 and 08/13/2025 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
1. Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 7, and 13, “A method”, “A computing device cluster”, and “A non-transitory machine-readable medium” are recited, which are each directed to one of the four statutory categories of invention (process, machine, and article of manufacture; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea (Step 2A Prong 2: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
obtaining, from a user, a combined sensitive term expression comprising a logical operator and a plurality of terms: a person writes down an expression from a user (e.g. ‘###’ AND ‘–‘ AND ‘##’ AND ‘-‘ AND ‘####’, which could for example correspond to a SSN, potential sensitive term)
generating a combined sensitive term library comprising a plurality of combined sensitive term entries, the plurality of combined sensitive term entries comprising a preset candidate combined sensitive term entry and a user combined sensitive term entry generated by parsing the combined sensitive term expression: a person writes down a library of combined sensitive terms combining the user’s terms with predefined terms
detecting, based on the combined sensitive term library, a to-be-detected text to obtain a first matching result indicating a hit combined sensitive term entry of the to-be-detected text in the combined sensitive tern library: a person compares a term with the terms in library to see if there is a match
and presenting the first matching result to the user: a user writes down the result and shows it to user
Claims 1, 7, and 13 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional elements are “A computing device cluster, comprising at least one computing device, wherein each computing device of the at least one computing device comprises a processor and a memory; and the processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, to cause the computing device cluster to…” (claim 7), and “A non-transitory machine-readable medium having instructions stored therein, which when executed by at least one computing device, cause the at least one computing device to” (claim 13). These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claims as a whole, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract idea. Therefore, claims 1, 7, and 13 are directed to abstract ideas.
Claims 1, 7, and 13 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations are mere instructions to implement the judicial exception using a generic computer, which even when viewed in combination with the claims as a whole, do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 1, 7, and 13 are not patent eligible.
Regarding claims 2-6, 8-12, and 14-18, “The method”, “The computing device cluster”, and “The non-transitory machine-readable medium” are recited, which are each directed to one of the four statutory categories of invention (process, machine, and article of manufacture; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite further mental processes which fall into the category of abstract idea (Step 2A Prong 2: YES).
The following limitations, under their broadest reasonable interpretation, recite further mental processes:
Claims 2, 8, and 14:
Claims 2, 8, and 14 recite “wherein the preset candidate combined sensitive term entry is extracted from a pre-configured training sample using an artificial intelligence (AI) technology” which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 3, 9, and 15:
wherein after the presenting the first matching result to the user, the method further comprising: detecting a review operation performed by the user on the first matching result, and determining an incorrectly hit combined sensitive term entry annotated by the user; and filtering the combined sensitive term library based on the incorrectly hit combined sensitive term entry: a person listens to user feedback about a wrong result, and filters the library (e.g. removes an incorrect term/pattern)
Claims 4, 10, and 16:
and presenting the second matching result to the user: a person presents a second match to a user
Claims 4, 10, and 16 recite “invoking an AI detection model to detect the to-be-detected text to obtain a second matching result indicating a hit combined sensitive term entry of the to-be-detected text in the combined sensitive term library, and the AI detection model is a pre-trained model configured to detect a combined sensitive term entry existing in a text”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 5, 11, and 17:
constructing a dictionary tree based on the plurality of combined sensitive term entries in the combined sensitive term library: a person writes down a tree for the library (e.g. branches of terms which are connected by the logical operators)
invoking an AC automaton to obtain a candidate term sequence in the to-be-detected text, wherein the candidate term sequence comprises one or more terms in the to-be-detected text: a person can follow the steps of the Aho-Corasick algorithm
…and performing matching on the candidate term sequence in the dictionary tree, and determining a combined sensitive term entry matching a part or all of terms in the candidate term sequence, wherein the combined sensitive term entry matching the part or all of terms in the candidate term sequence is the hit combined sensitive term entry: a person looks at the text and the tree and compares it to see if there is a match
Claims 6, 12, and 18:
wherein the combined sensitive term expression comprises one or more of AND, OR, NOT, and an operator representing preferential calculation: a person writes down sensitive term expression which contain Boolean operators
Claims 2-6, 8-12, and 14-18 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer, which even when viewed with the claims as a whole, do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract ideas. Therefore, claims 2-6, 8-12, and 14-18 are directed to abstract ideas.
Claims 2-6, 8-12, and 14-18 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer, which even when viewed with the claims as a whole, do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 2-6, 8-12, and 14-18 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
2. Claims 1, 6-7, 12-13, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Atreya et al. (US 2019/0228186 A1, hereinafter Atreya).
Regarding claim 1, Atreya teaches A method of combined sensitive term detection (Abstract), comprising: obtaining, from a user, a combined sensitive term expression (user defined objects/entity definitions: para. 0011 “At the client system, the end user may define new objects or modify/customize/upgrade the predefined objects for additional entity definitions to identify sensitive or confidential information in content within the computing environment. New objects defined by the new user may be added to the predefined external set of objects. This allows for extensibility of the predefined objects.”; para. 0115 “… Each object 360 may include a pattern, a term, a dictionary of words or phrases, an entity definition, a classifier, or any other structure used to identify confidential or sensitive information in content as detailed previously in Section B…The entity definition may specify a content type and one or more regular expressions associated with the content type, and may correspond to the entity definition 265 as described in Section B…”) comprising a logical operator and a plurality of terms (para. 0080 “The entity definition for the content type 270 may include one or more Boolean expressions 275A-1 to 275M-N (hereinafter generally referred to as Boolean expression 275). Each Boolean expression 275 (sometimes referred to as “regular expression”) of the content type 270 may specify one or more Boolean operators for a plurality of operands.”; para. 0081 “Each operand of the Boolean expression 275 of the entity definition may include a matching element used to matching against the content 230 undergoing classification to one of the content types 270. Each operand for the matching element may correspond to one of a pattern, a term, a dictionary of words or phrases, or a reference to another entity definition to match against the content 230.”); generating a combined sensitive term library comprising a plurality of combined sensitive term entries, the plurality of combined sensitive term entries comprising a preset candidate combined sensitive term entry and a user combined sensitive term entry generated by parsing the combined sensitive term expression (Fig. 3A, 345, comprising plurality of objects 360A-N; para. 0112 “At the client system, the end user may define new objects or modify predefined objects for additional entity definitions to identify sensitive or confidential information in content within the computing environment. New objects defined by the new user may be added to the predefined external set of objects. …Conversely, if all the corresponding identifiers and the signatures match, the evaluation engine may determine that the predefined external set of objects has not been tampered. The evaluation engine may then proceed to analyze content within the computing environment using the predefined internal set of objects plus the newly defined objects to identify any confidential or sensitive information in the content.”); detecting, based on the combined sensitive term library, a to-be-detected text to obtain a first matching result indicating a hit combined sensitive term entry of the to-be-detected text in the combined sensitive tern library (Fig. 2B, steps 284-288; para. 0105 “Referring to (288), and in further detail, the method 280 may include classifying, by the entity engine, the first content into a first content type of the plurality of content types, corresponding to the first entity definition, based on matching the matching element of the first operand to the first content, and matching other operands of the first entity definition to the first content. If the strings of characters of the first content are determined not to match with all of the matching elements of the remaining operands as specified by the operators of the Boolean expression, the entity engine may determine not to classify the first content as the first content type. Conversely, if the strings of character of the first content are determined to match with all the remaining elements of the operands as specified by the operators of the Boolean expression, the entity engine may classify the first content into the first content type.”); and presenting the first matching result to the user (para. 0107 “Referring to (290), and in further detail, the method 280 may include managing, by the entity engine, the first content for data loss prevention according to a severity level assigned to the first content type. The entity engine may manage the first content for data loss prevention to prevent data breach or exfiltration by the application in the computing environment. In some embodiments, the entity engine may assign a severity level to each content type of the entity definitions. The severity level may be predefined based on the content type. The severity level may also indicate a degree of sensitivity or confidentiality of the type of the information corresponding to the content type. The entity engine may perform a set of actions on the content for data loss prevention in accordance to the severity level of the content type to which the content is classified into. The set of actions may include warning the user of potential data breach (e.g., by displaying a prompt)…”).
Regarding claim 6, Atreya discloses wherein the combined sensitive term expression comprises one or more of AND, OR, NOT, and an operator representing preferential calculation (para. 0080 “The entity definition for the content type 270 may include one or more Boolean expressions 275A-1 to 275M-N (hereinafter generally referred to as Boolean expression 275). Each Boolean expression 275 (sometimes referred to as “regular expression”) of the content type 270 may specify one or more Boolean operators for a plurality of operands. The one or more Boolean operators may include disjunction (“OR”), conjunction (“AND”), negation (“NOT”), exclusive disjunction (“XOR”), alternative denial (“NOR”), joint denial (“NAND”), material implication (“If . . . then”), converse implication (“Not . . . without”), and/or bi-conditional (“If and only if”), among other”).
Regarding claim 7, claim 7 is a computing device cluster claim with limitations similar to those recited in method claim 1, and thus is rejected under similar rationale.
Additionally, Atreya discloses A computer device cluster, comprising at least one computer device (para. 0051 “The client 102 and server 106 may be deployed as and/or executed on any type and form of computing device, e.g. a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein. FIGS. 1C and 1D depict block diagrams of a computing device 100 useful for practicing an embodiment of the client 102 or a server 106. As shown in FIGS. 1C and 1D, each computing device 100 includes a central processing unit 121, and a main memory unit 122.”), wherein each computing device of the at least one computing device comprises a processor and a memory (para. 0051 “As shown in FIGS. 1C and 1D, each computing device 100 includes a central processing unit 121, and a main memory unit 122.”); and the processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, to cause the computing device cluster to (para. 0052 “The central processing unit 121 is any logic circuitry that responds to and processes instructions fetched from the main memory unit 122.”).
Regarding claim 12, claim 12 is rejected for analogous reasons to claim 6.
Regarding claim 13, claim 13 is a non-transitory machine-readable medium claim with limitations similar to those recite in method claim 1, and thus is rejected under similar rationale.
Additionally, Atreya discloses A non-transitory machine-readable medium having instructions stored therein, which when executed by at least one computing device, cause the at least one computing device to (para. 0133 “Modules may be implemented in hardware and/or as computer instructions on a non-transient computer readable storage medium, and modules may be distributed across various hardware or computer based components.”; para. 0134 “In addition, the systems and methods described above may be provided as one or more computer-readable programs or executable instructions embodied on or in one or more articles of manufacture. The article of manufacture may be a floppy disk, a hard disk, a CD-ROM, a flash memory card, a PROM, a RAM, a ROM, or a magnetic tape. In general, the computer-readable programs may be implemented in any programming language, such as LISP, PERL, C, C++, C#, PROLOG, or in any byte code language such as JAVA. The software programs or executable instructions may be stored on or in one or more articles of manufacture as object code.”).
Regarding claim 18, claim 18 is rejected for analogous reasons to claim 2.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3. Claims 2, 8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Atreya in view of Bali (US 2022/0036003 A1, hereinafter Bali).
Regarding claim 2, Atreya does not specifically disclose [wherein the preset candidate combined sensitive term entry] is extracted from a pre-configured training sample using an artificial intelligence (AI) technology.
Bali teaches wherein the preset candidate combined sensitive term entry is extracted from a pre-configured training sample using an artificial intelligence (AI) technology (para. 0038 “FIG. 3 is a flowchart illustrating a process 300 for training a neural network model to detect personal information in accordance with one or more example embodiments.”; para. 0040 “A plurality of tokens is formed for the selected target sentence (operation 304)”; para. 0041 “Thereafter, part of speech (POS) tagging is performed on the plurality of tokens (operation 306). POS tagging includes identifying the POS for each applicable token of the plurality of tokens. In one or more examples, this POS tagging is performed using a natural language processing system that integrated as part of or in communication with the detection system 101 in FIG. 1.”; see Fig. 6, combined term expression generated for a sentence).
Atreya and Bali are considered to be analogous to the claimed invention as they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Atreya to incorporate the teachings of Williamson in order to specifically have the combined sensitive term entry be extracted from a pre-configured training sample using an artificial intelligence (AI) technology. Doing so would be beneficial, as this would allow for sensitive term detection analysis to be performed for raw corpus data of various formats (Bali, para. 0024-0025) and converted into a standardized format understandable by the system for identifying sensitive terms (Fig. 6).
4. Claims 3-4, 9-10, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Atreya in view of Williamson et al. (US 2018/0232528 A1, hereinafter Williamson).
Regarding claim 3, Atreya does not specifically disclose detecting a review operation performed by the user on the first matching result, and determining an incorrectly hit combined sensitive term entry annotated by the user; and filtering the combined sensitive term library based on the incorrectly hit combined sensitive term entry.
Williamson teaches detecting a review operation performed by the user on the first matching result, and determining an incorrectly hit combined sensitive term entry annotated by the user (para. 0077 “To reduce the rates of false positives and false negatives, the classifier accuracy tuner 302 may prompt for or receive user feedback indicating whether reported sensitive data portions are actually sensitive data and whether data portions reported not to be sensitive data are actually sensitive data. For (each) feedback indicating that a determination of sensitive data was made incorrectly, the classifier accuracy tuner 302 analyzes the underlying data portion and the combination of components of the data classifier 108 that were used to make the erroneous determination…”); and filtering the combined sensitive term library based on the incorrectly hit combined sensitive term entry (para. 0077 “The classifier accuracy tuner 302 may present to the user the process used by that component to make the determination. This process may include a rule, pattern, context, metadata, or other process used by that component. The classifier accuracy tuner 302 may allow the user to modify or remove that process in order to improve the determination made by that component. For example, the user may be able to modify the pattern used to recognize sensitive data type to avoid a false positive.”).
Atreya and Williamson are considered to be analogous to the claimed invention as they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Atreya to incorporate the teachings of Williamson in order to specifically detect a review operation perform by the user for an incorrectly hit combined sensitive term entry, and to filter the combined sensitive term library based on the incorrectly hit combined sensitive term entry. Doing so would be beneficial, as this would help to identify false positives and false negatives from the classifier, leading to better accuracy over time (Willimason, para. 0075, 0077).
Regarding claim 4, Atreya does not specifically disclose invoking an AI detection model to detect the to-be-detected text to obtain a second matching result indicating a hit combined sensitive term entry of the to-be-detected text in the combined sensitive term library, and the AI detection model is a pre-trained model configured to detect a combined sensitive term entry existing in a text; and presenting the second matching result to the user.
Williamson teaches invoking an AI detection model to detect the to-be-detected text to obtain a second matching result indicating a hit combined sensitive term entry of the to-be-detected text in the combined sensitive term library, and the AI detection model is a pre-trained model configured to detect a combined sensitive term entry existing in a text (para. 0036 “The data classifier 108 may also determine that data is sensitive using machine learning algorithms. The data classifier 108 trains a machine learning model, such as a multilayer perceptron or convolutional neural network, on data known to be sensitive data. Features may first be extracted from the data using an N-gram (e.g., a bigram) model and these features input into the machine learning model for training. After training, the machine learning model will be able to determine (with a confidence level) whether data is sensitive or not. The trained machine learning model may be verified using a verification dataset composed of real world customer data, and the error rate analyzed. The machine learning model may be further improved during live operation by user feedback.”)); and presenting the second matching result to the user (para. 0044 “The data classification reporting module 114 reports the results produced by the data classifier 108 indicating whether data portions in the input data sources 102A-N are sensitive or not. The data classification reporting module 114 may present the results in a user interface to the user.”).
Atreya and Williamson are considered to be analogous to the claimed invention as they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Atreya to incorporate the teachings of Williamson in order to specifically invoke an AI detection model to detect the to-be-detected text to obtain a second matching result, and to present the second matching result to the user. Doing so would be beneficial, as AI models can be trained and refined to provide accurate detection results (Williamson, para. 0042).
Regarding claim 9, claim 9 is rejected for analogous reasons to claim 3.
Regarding claim 10, claim 10 is rejected for analogous reasons to claim 4.
Regarding claim 15, claim 15 is rejected for analogous reasons to claim 3.
Regarding claim 16, claim 16 is rejected for analogous reasons to claim 4.
5. Claims 5, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Atreya in view of Zhang & Ma (US 2023/0015054 A1, hereinafter Zhang).
Regarding claim 5, Atreya does not specifically disclose constructing a dictionary tree based on the plurality of combined sensitive term entries in the combined sensitive term library; invoking an AC automaton to obtain a candidate term sequence in the to-be-detected text, wherein the candidate term sequence comprises one or more terms in the to-be-detected text; and performing matching on the candidate term sequence in the dictionary tree, and determining a combined sensitive term entry matching a part or all of terms in the candidate term sequence, wherein the combined sensitive term entry matching the part or all of terms in the candidate term sequence is the hit combined sensitive term entry.
Zhang teaches constructing a dictionary tree based on the plurality of combined sensitive term entries in the combined sensitive term library (para. 0040 “When a sensitive word is detected through the AC automaton in step 2, first a trie is created by using a sensitive-word dictionary. In this embodiment, the trie is created with an example in which a dictionary includes multiple words [ embedded image]. As shown in FIG. 2, the greatest function of the trie is to store the words in the dictionary except that these words are expressed in the form of a tree. As shown in FIG. 3, then fail pointers are added on the basis of the trie.”); invoking an AC automaton to obtain a candidate term sequence in the to-be-detected text, wherein the candidate term sequence comprises one or more terms in the to-be-detected text (see Fig. 4 and para. 0042, input Chinese character string is input to AC automaton; para. 0030 “A text classification method is provided. The method includes the steps below. [0031] In step 1, the to-be-tested text is acquired, and then steps 2 and 3 are performed simultaneously. [0032] In step 2, a sensitive word is detected through an Aho-Corasick (AC) automaton, and then step 4 is performed.”); and performing matching on the candidate term sequence in the dictionary tree, and determining a combined sensitive term entry matching a part or all of terms in the candidate term sequence, wherein the combined sensitive term entry matching the part or all of terms in the candidate term sequence is the hit combined sensitive term entry (matches are determined via AC automaton using dictionary tree, see para. 0042).
Atreya and Zhang are considered to be analogous to the claimed invention as
they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Atreya to incorporate the teachings of Zhang in order to specifically construct a dictionary tree based on the plurality of combined sensitive term entries, to invoke an AC automaton to obtain a candidate term sequence in the to-be-detected text, and to perform matching on the candidate term sequence in the dictionary tree. Doing so would be beneficial, as the Aho-Corasick algorithm allows for fast transitions between failed string matches to other branches, allowing the automaton to transition between string matches without backtracking and with linear runtime complexity (see Mantin, US 2022/0207183 A1, para. 0030).
Regarding claim 11, claim 11 is rejected for analogous reasons to claim 5.
Regarding claim 17, claim 17 is rejected for analogous reasons to claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Baker & Padilla (US 12,061,718): detecting and managing sensitive information, receiving user feedback as to whether result was correctly identified as being sensitive (Fig. 3)
Medalion et al. (US 2021/0125615 A1): use pattern-based matching and machine learning based PII detection and removal system (Fig. 1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CODY DOUGLAS HUTCHESON whose telephone number is (703)756-1601. The examiner can normally be reached M-F 8:00AM-5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre-Louis Desir can be reached at (571)-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CODY DOUGLAS HUTCHESON/Examiner, Art Unit 2659
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
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