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
Response to Amendment
This is a reply to the application filed on 4/13/2026, in which, claim(s) 1-20 is/are pending.
Response to Arguments
Claim Rejections - 35 U.S.C. § 101:
Applicants’ arguments with respect to claim(s) 1-20 have been fully considered and are persuasive. The rejection of 35 USC §101 have been withdrawn in view of the amendment to claim.
Claim Rejections - 35 U.S.C. § 102 and 35 U.S.C. § 103:
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 reciting “wherein the data type of the first data is text and the second data type is imagery…”. It is unclear how both first and second data is imagery, which is the same type of data, while claim 1 specifying that they are different data type.
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.
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.
Claim(s) 1-12 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carmi et al. (US 20180089313 A1; hereinafter Carmi) in view of Mui et al. (US 20250278419 A1; hereinafter Mui) further in view of Ardhanari et al. (US 20210248268 A1; hereinafter Ardhanari).
Regarding claims 1, 10 and 18, Carmi discloses a method, comprising:
selecting a first augmentation pipeline to process first data based upon a data type of the first data (selecting the transcriber to transcribe the data via various sources [Carmi; ¶13-15; Figs. 1, 3 and associated text]). Carmi does not explicilty discloses selecting, from among a plurality of augmentation pipelines, a first augmentation pipeline to process first data based upon a data type of the first data, wherein a second augmentation pipeline of the plurality of augmentation pipelines is associated with processing a second data type different than the data type of the first data; however, in a related and analogous art, Mui teaches this feature.
Mui discloses multiple augmentation in which different one would process different types of inputs, such as document, words, graph, etc., [Mui; ¶30-40, 49-53; Figs. 2, 4 and associated texts]. It would have been obvious before the effective filing date of the claimed invention to modify Carmi in view of Mui with the motivation to generate both accurate and satisfying results [Mui; ¶12].
performing, by the first augmentation pipeline, entity tagging to assign tags to tokens within the first data to create tagged tokens that are tagged as either being entity tokens or non-entity tokens (the private information may be identified as described in further detail with respect to the method in FIG. 3 through the application of private information rules. Additionally, embodiments may receive an ontology, which may include the private information rules described in further detail herein, but may also include other language models or interpretation techniques or tools specifically crafted or tailored to a domain of the communication data to be analyzed. The identification of private information at exemplarily results in tagging of the identified pieces of private information. In an embodiment, the private information is tagged with an identification of the specific type of private information that the actual text for the transpiration represents. In non-limiting embodiments such tags may identify whether the private information is a phone number, credit card number, social security number, an account number, a birth date, or a password [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
generating a first contextual prompt, inputtable into a large language model, based upon (i) the tagged tokens and (ii) privacy regulations of at least one of a source region or a destination region (processing the transcribe data using the transcription and ontology, which may include the private information rules described in further detail herein, but may also include other language models or interpretation techniques or tools specifically crafted or tailored to a domain of the communication data to be analyzed. The identification of private information at exemplarily results in tagging of the identified pieces of private information. In an embodiment, the private information is tagged with an identification of the specific type of private information that the actual text for the transpiration represents, enable the automated review of either previously redacted communication information or the review and analysis of recorded and un-redacted communication data, in order to meet compliance with privacy and confidential information standards, laws, and regulations [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
processing the first contextual prompt using the large language model to identify one or more tagged tokens to mask (The tag transcription is then provided to a rule engine to evaluate the compliance of the transcription with internal, legal, or regulatory private and confidential information standards for these standards may each be different and the application of differing standards may depend upon the manner of use of the transpiration or the intended manner of storage [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
masking the one or more tagged tokens within the first data to create augmented first data (the tagged private information may be removed completely, while in an alternative embodiment, the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file [Carmi; ¶16-18; Figs. 1, 3 and associated text]). Carmi discloses removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript. Carmi-Mui combination does not explicilty discloses transmitting the augmented first data to a computing device within the destination region; however, Ardhanari teaches this feature.
In particular, Ardhanari teaches filtering and transmitting the data based on the region regulations [Ardhanari; ¶145, 178]. It would have been obvious before the effective filing date of the claimed invention to modify Carmi-Mui combination in view of Ardhanari with the motivation share data based on set region regulation.
Regarding claim 2, Carmi-Mui-Ardhanari combination discloses the method of claim 1, comprising:
tokening, by the first augmentation pipeline, the first data to identify the tokens (transcribe the content and identified the private information, tagging the identified information [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
performing, by the first augmentation pipeline, part of speech tagging to tag the tokens with part of speech tags to create tagged tokens (speech analytics to transcription data and tagging information based on rules and compliances [Carmi; ¶13-15; Figs. 1, 3 and associated text]); and
processing raw text of the first data and the tagged tokens to identify the entity tokens and the non-entity tokens (processing the transcribe data using the transcription and ontology, which may include the private information rules described in further detail herein, but may also include other language models or interpretation techniques or tools specifically crafted or tailored to a domain of the communication data to be analyzed. The identification of private information at exemplarily results in tagging of the identified pieces of private information. In an embodiment, the private information is tagged with an identification of the specific type of private information that the actual text for the transpiration represents, enable the automated review of either previously redacted communication information or the review and analysis of recorded and un-redacted communication data, in order to meet compliance with privacy and confidential information standards, laws, and regulations [Carmi; ¶13-15; Figs. 1, 3 and associated text]).
Regarding claim 3, Carmi-Mui-Ardhanari combination discloses the method of claim 1, comprising: evaluating source privacy regulations of the source region and destination privacy regulations of the destination region to identify a set of entities to mask; and in response to a tagged token corresponding to an entity within the set of entities to mask, masking the tagged token (the tagged private information may be removed completely, while in an alternative embodiment, the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file based on compliance rule [Carmi; ¶14-18; Figs. 1, 3 and associated text]).
Regarding claim 4, Carmi-Mui-Ardhanari combination discloses the method of claim 1, wherein the data type of the first data is text and the second data type is imagery (multiple augmentation in which different one would process different types of inputs, such as document, words, graph, etc., [Mui; ¶30-40, 49-53; Figs. 2, 4 and associated texts]. The motivation to generate both accurate and satisfying results [Mui; ¶12].
Regarding claim 5, Carmi-Mui-Ardhanari combination discloses the method of claim 1, comprising:
selecting a second augmentation pipeline to process second data based upon a data type of the second data (selecting the transcriber to transcribe the data via various sources [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
identifying, by the second augmentation pipeline, objects within the second data (the private information may be identified as described in further detail with respect to the method in FIG. 3 through the application of private information rules. Additionally, embodiments may receive an ontology, which may include the private information rules described in further detail herein, but may also include other language models or interpretation techniques or tools specifically crafted or tailored to a domain of the communication data to be analyzed. The identification of private information at exemplarily results in tagging of the identified pieces of private information. In an embodiment, the private information is tagged with an identification of the specific type of private information that the actual text for the transpiration represents. In non-limiting embodiments such tags may identify whether the private information is a phone number, credit card number, social security number, an account number, a birth date, or a password [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
classifying the objects with labels identifying the objects to create labeled objects (specific type of the private information is selected from the group [Carmi; ¶13-15; Figs. 1, 3 and associated text]); and
identifying a set of entities to mask based upon the privacy regulations (The tag transcription is then provided to a rule engine to evaluate the compliance of the transcription with internal, legal, or regulatory private and confidential information standards for these standards may each be different and the application of differing standards may depend upon the manner of use of the transpiration or the intended manner of storage [Carmi; ¶13-15; Figs. 1, 3 and associated text]); and
processing, by a masking engine, the second data and the set of entities to mask to generate augmented second data to transmit to a destination computing device at the destination region (the tagged private information may be removed completely, while in an alternative embodiment, the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file [Carmi; ¶16-18; Figs. 1, 3 and associated text], filtering and transmitting the data based on the region regulations [Ardhanari; ¶145, 178]. The motivation share data based on set region regulation.
Regarding claim 6, Carmi-Mui-Ardhanari combination discloses the method of claim 5, wherein the second data comprises visual data, and wherein a subset of the visual data is masked to create the augmented second data (visual representation or snapshot of the data [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 7, Carmi-Mui-Ardhanari combination discloses the method of claim 5, comprising: inputting the augmented second data into at least one of image classification functionality, image segmentation functionality, object tracking functionality, pose estimation functionality, image parsing functionality, or process automations functionality (The augmented curation of health records converts a raw health record, such as an electronic health records (EHR), a patient chart, or the like, into a structured representation of a patient phenotype (e.g., a snapshot of a patient's symptoms, diagnoses, treatments, or the like). The structured representation may then be visualized, used as an input for statistical or machine learning analysis, or the like [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 8, Carmi-Mui-Ardhanari combination discloses the method of claim 1, comprising: inputting the augmented first data into at least one of a chatbot, an intent identification model, a churn propensity model, market analysis functionality, variable regression, or functionality that generates instructions for controlling network equipment of a communication network (The data may be visualized, used as an input for statistical or machine learning analysis, or the like [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 9, Carmi-Mui-Ardhanari combination discloses the method of claim 1, wherein the first data comprises text, and wherein a subset of the text is masked to create the augmented first data (the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file [Carmi; ¶16-18; Figs. 1, 3 and associated text]).
Regarding claim 11, Carmi-Mui-Ardhanari combination discloses the system of claim 10, wherein the operations further comprise: inputting the augmented first data into at least one of image classification functionality, image segmentation functionality, object tracking functionality, pose estimation functionality, image parsing functionality, or process automations functionality (The data may be visualized, used as an input for statistical or machine learning analysis, or the like [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 12, Carmi-Mui-Ardhanari combination discloses the system of claim 10, wherein the first data comprises visual data, and wherein a subset of the visual data is masked to create the augmented first data (the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file [Carmi; ¶16-18; Figs. 1, 3 and associated text], healthcare data is to anonymize or mask the private data attributes, e.g., mask social security numbers before it is processed or analyzed. In some embodiments of the present disclosures, methods may be employed for masking and de-identifying personal information from healthcare records. Using these methods, a dataset containing healthcare records may have various portions of its data attributes masked or de-identified. The resulting dataset thus may not contain any personal or private information that can identify one or more specific individuals [Ardhanari; ¶173-176]). The motivation share data based on set region regulation.
Regarding claim 14, Carmi-Mui-Ardhanari combination discloses the system of claim 10, wherein the operations further comprise: utilizing a neural network model to segment boundaries within the first data to identify the objects (The data may be visualized, used as an input for statistical or machine learning analysis, or the like [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 15, Carmi-Mui-Ardhanari combination discloses the system of claim 10, wherein the operations further comprise: generating a contextual prompt for a model based upon the privacy regulations; and processing the contextual prompt using the model to identify the set of entities (receive an ontology, which may include the private information rules described in further detail herein, but may also include other language models or interpretation techniques or tools specifically crafted or tailored to a domain of the communication data to be analyzed [Carmi; ¶14-15; Figs. 1, 3 and associated text]).
Regarding claim 16, Carmi-Mui-Ardhanari combination discloses the system of claim 10, wherein the operations further comprise:
selecting a second augmentation pipeline to process second data based upon a data type of the second data (selecting the transcriber to transcribe the data via various sources [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
identifying, by the second augmentation pipeline, objects within the second data (the private information may be identified as described in further detail with respect to the method in FIG. 3 through the application of private information rules. Additionally, embodiments may receive an ontology, which may include the private information rules described in further detail herein, but may also include other language models or interpretation techniques or tools specifically crafted or tailored to a domain of the communication data to be analyzed. The identification of private information at exemplarily results in tagging of the identified pieces of private information. In an embodiment, the private information is tagged with an identification of the specific type of private information that the actual text for the transpiration represents. In non-limiting embodiments such tags may identify whether the private information is a phone number, credit card number, social security number, an account number, a birth date, or a password [Carmi; ¶13-15; Figs. 1, 3 and associated text]);
classifying the objects with labels identifying the objects to create labeled objects (specific type of the private information is selected from the group [Carmi; ¶13-15; Figs. 1, 3 and associated text]); and
identifying a set of entities to mask based upon the privacy regulations (The tag transcription is then provided to a rule engine to evaluate the compliance of the transcription with internal, legal, or regulatory private and confidential information standards for these standards may each be different and the application of differing standards may depend upon the manner of use of the transpiration or the intended manner of storage [Carmi; ¶13-15; Figs. 1, 3 and associated text]); and
processing, by a masking engine, the second data and the set of entities to mask to generate augmented second data to transmit to a destination computing device at the destination region (the tagged private information may be removed completely, while in an alternative embodiment, the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file [Carmi; ¶16-18; Figs. 1, 3 and associated text], filtering and transmitting the data based on the region regulations [Ardhanari; ¶145, 178]. The motivation share data based on set region regulation.
Regarding claim 17, Carmi-Mui-Ardhanari combination discloses the system of claim 16, wherein the operations further comprise: inputting the augmented second data into at least one of a chatbot, an intent identification model, a churn propensity model, market analysis functionality, variable regression, or functionality that generates instructions for controlling network equipment of a communication network (The data may be visualized, used as an input for statistical or machine learning analysis, or the like [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 19, Carmi-Mui-Ardhanari combination discloses the non-transitory computer-readable medium of claim 18, wherein the operations further comprise: inputting the augmented data into at least one of a chatbot, an intent identification model, a churn propensity model, market analysis functionality, variable regression, or functionality that generates instructions for controlling network equipment of a communication network (The data may be visualized, used as an input for statistical or machine learning analysis, or the like [Ardhanari; ¶289-295]). The motivation share data based on set region regulation.
Regarding claim 20, Carmi-Mui-Ardhanari combination discloses the non-transitory computer-readable medium of claim 18, wherein the operations further comprise: evaluating source privacy regulations of the source region and destination privacy regulations of the destination region to identify a set of entities to mask; and in response to a tagged token corresponding to an entity within the set of entities to mask, masking the tagged token (the tagged private information may be removed completely, while in an alternative embodiment, the tagged private information may be replaced with the name of the tag such that the context of the private information conveyed in the interpersonal communication is maintained while the private information is removed from the file based on compliance rule [Carmi; ¶14-18; Figs. 1, 3 and associated text]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carmi-Mui-Ardhanari combination in view of Guegan Marat et al. (US 20250232059 A1; hereinafter Marat)
Regarding claim 13, Carmi-Mui-Ardhanari combination does not explicilty discloses the system of claim 10, wherein the operations further comprise: detecting a gradient shift within the first data; creating a bounding box around an object based upon the gradient shift; and assigning the label to the bounding box; however, in a related and analogous art, Marat teaches these features.
In particular, Marat teaches detecting of object based on gradient parameters, generating of bounding box on the object with respect the input image and vector defining a respective bounding box; thus, performing privacy-preserving federated for object detected [Marat; ¶30-40, 60-76; Fig. 2 and associated text]. It would have been obvious before the effective filing date of the claimed invention to modify Carmi-Mui-Ardhanari combination in view of Marat with the motivation to prevents data leakage, thereby enhancing privacy [Marat; ¶130].
Internet Communications
Applicant is encouraged to submit a written authorization for Internet communications (PTO/SB/439, http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) in the instant patent application to authorize the examiner to communicate with the applicant via email. The authorization will allow the examiner to better practice compact prosecution. The written authorization can be submitted via one of the following methods only: (1) Central Fax which can be found in the Conclusion section of this Office action; (2) regular postal mail; (3) EFS WEB; or (4) the service window on the Alexandria campus. EFS web is the recommended way to submit the form since this allows the form to be entered into the file wrapper within the same day (system dependent). Written authorization submitted via other methods, such as direct fax to the examiner or email, will not be accepted. See MPEP § 502.03.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAO Q HO whose telephone number is (571)270-5998. The examiner can normally be reached on 7:00am - 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 Nickerson can be reached on (469) 295-9235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAO Q HO/Primary Examiner, Art Unit 2432
Prosecution Insights