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 .
DETAILED ACTION
Claims 1-12 are pending in this office action.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 22, 2024, is 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.
Claims 1-12 are rejected under 35 U.S.C. 101 because they are directed to a judicial exception (an abstract idea) without adding significantly more to transform the exception into a patent-eligible application. The claims are directed to the abstract idea of Information Processing (Mental Processes and Certain Methods of Organizing Human Activity). Specifically, the claims focus on: Collecting data: Detecting Personal Identifiable Information (PII) from a user query. Manipulating/Analyzing data: Converting PII into "neutral information" or "masking" it. Storing/Transmitting data: Transmitting a "user-neutral query" and storing patterns in a database. The core concept—identifying sensitive information and replacing it with generic placeholders to protect privacy—is a long-standing human activity (e.g., a clerk redacting a physical document before public release). Under Electric Power Group, LLC v. Alstom S.A., the mere collection, analysis, and display of information are considered abstract ideas. Furthermore, the processes of "identifying context" and "generating a response" (Claims 2, 8) are functions that can be performed by the human mind, classifying them as mental processes. The claims do not recite an "inventive concept" that is "significantly more" than the abstract idea itself. The claims recite a "management server," "user devices," "communication module," "memory," and "processor." These are described as performing their well-understood, routine, and conventional functions (e.g., transmitting data, storing programs). The use of a "language model" or "Named Entity Recognition (NER)" (Claims 4, 10) represents the use of conventional mathematical algorithms and software tools to automate the abstract idea on a computer, which does not provide an inventive concept (Alice Corp.). The claims do not improve the functioning of the computer itself (e.g., a better operating system or faster hardware). Instead, they use the computer as a tool to process data more efficiently. The "on-device" limitation is a jurisdictional or "field-of-use" limitation that does not change the underlying abstract nature of the data processing. Converting PII to neutral information is a mathematical or linguistic manipulation of data. Claim 12’s training based on "location within a sentence structure" is a further refinement of data analysis but does not represent a technical solution to a technical problem in the computer's hardware or software architecture.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Heckel et al. (U.S. Patent No. 10,169,315) in view of Cosman et al. (U.S. Patent Pub. No. 2021/0192078).
Regarding claim 1, Heckel et al. teaches an on device-based system for suppressing the leakage of personal information and for providing personalized response, comprising: a plurality of user devices configured to detect Personal Identifiable Information (PII) from a user query and transmit a user-neutral query which is obtained by converting the PII into neutral information (col. 2, line 38 through col. 3, line 4 and col. 7, lines 19-37).
Heckel et al. does not teach a management server configured to receive the user-neutral query and train a management language model that generates a common response pattern for each neutral query pattern.
Cosman et al. teaches a management server configured to receive the user-neutral query and train a management language model that generates a common response pattern for each neutral query pattern (paragraph 0055).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine receive the user-neutral query and train a language model, as taught by Cosman et al., with the method of Heckel et al. It would have been obvious for such modifications because the neutral training ensures no individual identity is compromised.
Regarding claim 2, Heckel et al. as modified by Cosman et al. teaches wherein the user device includes: a response generation model configured to identify the context of the user query through natural language processing analysis and generate a response; and a PII detection model configured to generate the user-neutral query by masking the PII extracted from the user query or converting the extracted PII into predetermined words (see col. 7, lines 4-18 of Heckel et al. and paragraph 0045 of Cosman et al.).
Regarding claim 3, Heckel et al. as modified by Cosman et al. teaches wherein the response generation model is configured as a language model that is trained with a user query pattern based on the user query, and the user query pattern is learned for the same context based on a linguistic distribution of word frequencies and types in the user query (see paragraph 0037 and 0054 of Cosman et al.).
Regarding claim 4, Heckel et al. as modified by Cosman et al. teaches wherein the PII detection model determines whether entities including letters or words and composing the user query are personal information based on initial PII stored in a PII database, extracts the PII from the user query based on Named Entity Recognition (NER), and stores the extracted PII in the PII database (see paragraph 0089 of Cosman et al.).
Regarding claim 5, Heckel et al. teaches wherein the management language model is trained with PII patterns for the respective neutral query patterns by using the location of the neutral information within a sentence structure of the user-neutral query (col. 19, line 50 through col. 20, line 7).
Regarding claim 6, Heckel et al. teaches wherein the PII is classified into direct identifiers and quasi-identifiers to identify a specific individual (col. 3, line 51 through col. 4, line 5).
Regarding claim 7, Heckel et al. teaches an on device-based user device for suppressing the leakage of personal information and for providing personalized response, comprising: a communication module (fig. 9, ref. num 912); a memory that stores a personalized response program (fig. 9, ref. num 910); and a processor that executes the personalized response program (fig. 9, ref. num 911), wherein the personalized response program detects PII from a user query, converts the PII into neutral information (col. 2, line 38 through col. 3, line 4 and col. 7, lines 19-37).
Heckel et al. does not teach transmits a user-neutral query converted as the neutral information to a management server.
Cosman et al. teaches transmits a user-neutral query converted as the neutral information to a management server (paragraph 0055).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine receive the user-neutral query and train a language model, as taught by Cosman et al., with the method of Heckel et al. It would have been obvious for such modifications because the neutral training ensures no individual identity is compromised.
Regarding claim 8, Heckel et al. as modified by Cosman et al. teaches wherein the personalized response program includes: a response generation model configured to identify the context of the user query through natural language processing analysis and generate a response; and a PII detection model configured to generate the user-neutral query by masking the PII extracted from the user query or converting the extracted PII into predetermined words (see col. 7, lines 4-18 of Heckel et al. and paragraph 0045 of Cosman et al.).
Regarding claim 9, Heckel et al. as modified by Cosman et al. teaches wherein the response generation model is configured as a language model that is trained with a user query pattern based on the user query, and the user query pattern is learned for the same context based on a linguistic distribution of word frequencies and types in the user query (see paragraph 0037 and 0054 of Cosman et al.).
Regarding claim 10, Heckel et al. as modified by Cosman et al. teaches wherein the PII detection model determines whether entities including letters or words and composing the user query are personal information based on initial PII stored in a PII database, extracts the PII from the user query based on Named Entity Recognition (NER), and stores the extracted PII in the PII database (see paragraph 0089 of Cosman et al.).
Regarding claim 11, Heckel et al. teaches a personalized response method that is performed by an on device-based user device for suppressing the leakage of personal information and for providing personalized response, comprising: (a) receiving, by a management server, a user-neutral query, which is obtained by converting PII contained in a user query into neutral information, from a plurality of user devices (col. 2, line 38 through col. 3, line 4 and col. 7, lines 19-37).
Heckel et al. does not teach training, by the management server, a management language model that generates a common response pattern for each neutral query pattern.
Cosman et al. teaches training, by the management server, a management language model that generates a common response pattern for each neutral query pattern (paragraph 0055).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine receive the user-neutral query and train a language model, as taught by Cosman et al., with the method of Heckel et al. It would have been obvious for such modifications because the neutral training ensures no individual identity is compromised.
Regarding claim 12, Heckel et al. teaches wherein the management language model is trained with PII patterns for the respective neutral query patterns by using the location of the neutral information within a sentence structure of the user-neutral query (col. 19, line 50 through col. 20, line 7).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON HOFFMAN whose telephone number is (571)272-3863. The examiner can normally be reached Monday-Friday 8:30AM-5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Pwu can be reached at (571)272-6798. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRANDON HOFFMAN/Primary Examiner, Art Unit 2433