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
Claim Status
1. This is in response to application filed on 1/16/2024 in which claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
2. 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.
Claims 1, 9 and 15-16 recite the claimed limitations of "...the destination system...", however, there are insufficient prior antecedent basis for the limitation in the claims.
All claims that depend on the above rejected claims are also rejected for fully incorporating the deficiencies of the above rejected claims from which they depend.
For complete examination purposes, the Examiner will broadly address all of the above rejected claims in light of the overall concept of Applicant’s invention.
Appropriate corrections are therefore required.
Claim Rejections - 35 USC § 103
3. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 7-10, 14 and 16-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Gailloux et al., (US 9,781,255), (hereinafter, Gailloux) in view of Kent et al., (US 2018/0324297), (hereinafter, Kent).
Regarding claims 1 and 16, Gailloux discloses a computer-implemented method/system of assessing risks of calls without exposing caller automatic identification numbers (ANIs) to call destinations (= call centers may examine an automatic identification number (ANI) field of a call origination message to screen incoming calls; and call verification framework to reduce ANI spoofing, see col. 3, lines 23-39; and call verification to establish confidence that an incoming call is legitimate or spoof, see col. 5, lines 32-36), the method comprising:
receiving, by a computer of an analytics system, call data for a call from a calling device via a terminating carrier (= service provider may create call event at different points during a call, see col. 4, lines 33-34; enterprise call center may send a call verification request to service provider/service control point (SCP) associated with subject call origination message or to a server computer system that executes a call verification application, see col. 4, lines 52-56) the call data including telephony-protocol metadata indicating a caller ANI associated with the calling device (= service provider determines additional information about a subscriber and storing this information in metadata fields of the call origination message, see col. 3, lines 40-43; and the additional may comprise an indicator that the call originator identified in the ANI is authenticated or not authenticated, see col. 3, lines 55-57) and a destination identifier associated with a provider system
(= service provider may have access to information identifying the call originator that is separate from the ANI in the call origination message, for example access to a mobile equipment identity of a user equipment that originates the call origination message, see col. 3, lines 58-63; and the call verification request message identifies the called number of the enterprise call center, see col. 4, lines 56-58);
storing, by the computer, the call data into a request database of the analytics system (= SCP may store these events in an archive or data store for later analysis, see col. 4, lines 47-48), the call data including the telephony-protocol metadata and the destination identifier (= service provider determines additional information about a subscriber and storing this information in metadata fields of the call origination message, see col. 3, lines 40-43; and the call verification request message identifies the called number of the enterprise call center, see col. 4, lines 56-58).
Gailloux explicitly fails to disclose the claimed limitations of:
“generating, by the computer, one or more risk scores for the call by executing a machine- learning architecture on the call data; and transmitting, by the computer, a call connection instruction to the destination system based upon the one or more risk scores”.
However, Kent, which is an analogous art for example discloses that bad actors often hide their identity by withholding identifying information, pretending to be another entity as in spoofing another telephone number (see, [0004]) and a system for conducting risk assessments (see, [0007]).
Kent also discloses the claimed limitations of:
“generating, by the computer, one or more risk scores for the call by executing a machine- learning architecture on the call data (= risk assessment system includes a machine learning engine configured to generate a model for assessing risk of incoming calls based on a set of call log records and generate a category and a likelihood value, see [0007 and 0089]); and
“transmitting, by the computer, a call connection instruction to the destination system based upon the one or more risk scores” (= risk processing engine 206 of endpoint device 204 provides the category and likelihood value to the call handler engine 208, and then to block 548, where the call handler 208 processes the incoming call page using the category and likelihood value, where the incoming page may include presenting a prompt to end user to allow the end user to accept or reject the call; and blocking incoming call if reputation score is too low, see [0113 and 0036]; whereby the “destination system” includes the end user and the reputation value and likelihood value are being associated with the “risk score”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kent with Gailloux for the benefit of achieving a communication system that provides comprehensive reputation information for other callers to allow an end user to decide how a call should be handled.
Regarding claims 2 and 17, as mentioned in claims 1 and 16, Gailloux further discloses that the method/system further comprising obtaining, by the computer, a unique caller identifier associated with the caller ANI, wherein the call data stored into the request database includes the unique caller identifier associated with the caller ANI (see, col. 4, lines 52-61).
Regarding claims 3 and 18, as mentioned in claims 1 and 16, Gailloux explicitly fails to disclose that the method/system further comprising obtaining, by the computer from a portability database, portability data associated with the calling device, wherein the computer generates the one or more risk scores by executing the machine-learning architecture on the call data and the portability data associated with the calling device.
However, Kent which is an analogous art equivalently discloses the method/system further comprising obtaining, by the computer from a portability database, portability data associated with the calling device, wherein the computer generates the one or more risk scores by executing the machine-learning architecture on the call data and the portability data associated with the calling device (= machine learning engine 218 processes call log records stored in the call log data store 220 in order to generate models usable to predict a level of risk associated with incoming calls, see [0055]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kent with Gailloux for the benefit of achieving a communication system that provides comprehensive reputation information for other callers to allow an end user to decide how a call should be handled.
Regarding claim 7, as mentioned in claim 1, Gailloux further discloses the method, wherein the call data includes an invitation message according to a telephony protocol (= receive a call origination message from user equipment, see col. 10, lines 40-43).
Regarding claim 8, as mentioned in claim 1, Gailloux further discloses the method wherein receiving the call data includes: obtaining, by the computer, a unique caller identifier corresponding to the caller ANI; and appending, by the computer, the unique caller identifier to the call data, wherein the computer stores the unique caller identifier into the request database with the call data (see, col. 10, lines 49-65).
Regarding claim 9, as mentioned in claim 1, Gailloux further discloses the method, wherein the destination identifier includes at least one of: a destination ANI for the destination system or a phone number for the destination system (see, col. 4, lines 56-58).
Regarding claim 10, as mentioned in claim 1, Gailloux explicitly fails to disclose that the method further comprising receiving, by the computer from a server of the provider system, a score request for the one or more risk scores of the call.
However, Kent which is an analogous art equivalently discloses the method further comprising receiving, by the computer from a server of the provider system, a score request for the one or more risk scores of the call (= reputation score, see [0036]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kent with Gailloux for the benefit of achieving a communication system that provides comprehensive reputation information for other callers to allow an end user to decide how a call should be handled.
Regarding claim 14, as mentioned in claim 1, Gailloux explicitly fails to disclose that the method further comprising executing, by the computer, the machine-learning architecture on training call data of a plurality of training calls for a plurality of training devices to train the machine-learning architecture using a plurality of training labels corresponding to the plurality of training calls.
However, Kent which is an analogous art equivalently discloses the method further comprising executing, by the computer, the machine-learning architecture on training call data of a plurality of training calls for a plurality of training devices to train the machine-learning architecture using a plurality of training labels corresponding to the plurality of training calls (= machine learning engine 218 operates over labeled call log record, any suitable supervised learning technique may be used see, [0089]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kent with Gailloux for the benefit of achieving a communication system that provides comprehensive reputation information for other callers to allow an end user to decide how a call should be handled.
Regarding claim 15, as mentioned in claim 1, Gailloux explicitly fails to disclose that the method further comprising: detecting, by the computer, the caller ANI in the call data; and removing, by the computer, the caller ANI from the call data prior to transmitting the call data to the destination system.
However, Kent, which is an analogous art for example discloses a system for conducting risk assessments (see, [0007]); and also mentions that bad actors often hide their identity by withholding identifying information, pretending to be another entity as in spoofing another telephone number (see, [0004]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kent with Gailloux for the benefit of achieving a communication system that provides comprehensive reputation information for other callers to allow an end user to decide how a call should be handled.
Allowable Subject Matter
4. Claims 4-6, 11-13, 15 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CONCLUSION
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a. Djankovic et al., (US 2023/0209351) teaches assessing risk of fraud associated with user unique identifier using telecommunications data.
b. Jakobsson (US 2021/0258421) teaches validating automatic number identification data.
c. Barber et al., (US 2012/0100830) teaches anonymous party voice call processing.
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KWASI KARIKARI whose telephone number is (571)272-8566. The examiner can normally be reached M-Sat: 6am-10pm.
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, Charles Appiah can be reached on 571-272-7904. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Kwasi Karikari/
Primary Examiner: Art Unit 2641.