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, 3-14, and 16-20 are pending and being considered.
Claims 2 and 15 have been cancelled.
The title submitted on 07/6/2026 have been accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/06/2026 was filed after the mailing date of the application no. 19/415559. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Double Patenting
The double patenting rejection is withdrawn based on Terminal Disclaimer submitted on 07/06/2026.
Response to 101
The 101 rejection is withdrawn based on applicant’s amendments and further based on applicant’s arguments.
Response to 102/103
Applicant’s arguments filed on 07/06/2026 have been fully considered and are not persuasive.
In response to applicant’s arguments that on page 12 of remarks, the applicant argues that the cited prior art RAFFERTY fails to teach “receiving a message from the third party device via the RAFFERTY’s communication interface, via the communications network, the message comprising an indication of whether or not the identification number corresponds to a valid identity” the applicant argues that the RAFFERTY’s authentication word string (AWS) does not identify the user. Since the AWS does not identify anything therefore AWS cannot be reasonably interpreted as the claimed “identification number” the examiner respectfully disagrees because
The clam does not recite “identification number to identify the user” as argued by the applicant. The claim merely recites “indication of whether or not the identification number corresponds to a valid identity” the examiner notes that valid identity is not tied with user.
Even if the valid identity corresponds to the user, the references still teach identification number i.e., AWS for identifying user. See on [0103] “ the user 12 transmitting the authentication word string to the authentication server….. Authentication server 16 then attempts to authenticate the user on the basis of the received authentication word string AWS”. See on [0410-0411] teaches AWS is used to identify a user.
In response to applicant’s argument on page 13 of remarks, the applicant argues that Bachmann’s checksum evaluation is not within speech-based authentiaon framework. The examiner respectfully disagrees because Bachmann explicitly teaches checksum evolution of identification number within speech-based authentication framework. See on [0076] “ Speech recognition systems may further benefit from the various embodiments by providing a finite database of words that can be recognized in order to obtain verbal input of a very large number. Since the word lists are finite and the words entered in a particular order, computer systems may be configured to more easily recognize the mnemonic phrases than would be possible for receiving the letters and numbers of the corresponding file directly”. See also on [0043-0045] “ A user may input the mnemonic, such as by speaking into a voice recognition system or by typing the mnemonic into a keyboard, step 6. A computer then parses the phrase into the parts of speech corresponding to the word lists,”
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). In this case the applicant argues that checksum evolution to determine whether the list of wors corresponds to a valid identification number as disclosed by Bachmann, when combined with RAFFERTY’s teaching would be based on impressible hindsight. The examiner respectfully disagrees because Bachmann’s teaching enable users to remember the number, the hash value is used to create a unique combination of a plurality of smaller numbers that are used as word indices to identify particular words in a plurality of word lists in order to select a unique combination of words to form the mnemonic. One would be motivated to do so in order to identify particular words using checksum in a plurality of word lists in order to select a unique combination of words to form the mnemonic (BACHMANN [0004]).
Claim Objections
Claims 1, 19 and 20 objected to because of the following informalities:
Claims 1, 19 and 20 recites “the list of words” should read as “list of words” to overcome the potential antecedent issue.
Claims 1, 19 and 20 recites “evaluating a checksum……whether or not the list of words corresponds to valid identification number” the examiner suggests to clarify whether the identification number transmitted in subsequent limitation is based on checksum evaluation. Appropriate correction is required.
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, 3-8, 10-14 and 16-20 is rejected under 35 U.S.C. 103 as being unpatentable over RAFFERTY et al (hereinafter RAFFERTY) (US 20210081923) in view of BACHMANN et al (hereinafter BACHMANN) (US 20090313269).
Regarding claim 1 RAFFERTY teaches a method of authenticating a user to a third party, the method performed by at least one computing device and comprising: (RAFFERTY on [0001] teaches a method of and apparatus for authentication and authorization);
obtaining pre-seeding data (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
configuring a speech recognition service to perform automated speech recognition using the pre-seeding data (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
receiving an input, the input comprising an automated speech recognition result of the user’s speech during a voice-based communication session between the user and the third party, the automated speech recognition result comprising one or more words and obtained using the speech recognition service (RAFFERTY Fig 1a, Fig 1b and text on [0099-0103] teaches user 12 in communication with service provider and authentication server (i.e., third-party) equipped with voice recognition for receiving word spoken by user);
processing the automated speech recognition result using a processor of the at least one computing device to obtain an identification number corresponding to the one or more words (RAFFERTY on [0099-0103] teaches processing words spoken by user and generating authentication word string (i.e., identification number));
transmitting the identification number via a communication interface of the at least one computing device, via a communications network to a third party device associated with the third party (RAFFERTY on [0099-0103] teaches transmitting the authentication word string to authentication server);
and receiving a message from the third party device via the communication interface, via the communications network, the message comprising an indication of whether or not the identification number corresponds to a valid identity (RAFFERTY on [0099-0103] teaches authentication server 16 then attempts to authenticate the user on the basis of the received authentication word string AWS by comparison with an expected word string (EWS) and, if the match is successful, sends an authentication confirmation message or service request message directly to the service provider 18 and/or via the agent 14 to confirm that the request is legitimate and that the service may be provided to the user 12).
RAFFERTY fails to teach evaluating a checksum associated with the list of words to determine whether or not the list of words corresponds to a valid identification number, however BACHMANN from analogous art teaches evaluating a checksum associated with the list of words to determine whether or not the list of words corresponds to a valid identification number (BACHMANN on [0004 and 0037-0038] teaches examples of hash algorithms include known cryptographic hash algorithms, digital signatures, checksum values and cyclic redundancy check (CRC) algorithms used to confirm proper transmission of documents. Typical hash values generated by known hash algorithms are too large to remember. To enable users to remember the number, the hash value is used to create a unique combination of a plurality of smaller numbers that are used as word indices to identify particular words in a plurality of word lists in order to select a unique combination of words to form the mnemonic. This mnemonic then provides a memorable identifier for the document that can be used to uniquely identify the particular version).
Thus, it would have been obvious to one ordinary skill in the art before the effective filing date to implement the teaching of BACHMANN into the teaching of RAFFERTY by evaluating a checksum associated with the list of words. One would be motivated to do so in order to identify particular words using checksum in a plurality of word lists in order to select a unique combination of words to form the mnemonic (BACHMANN [0004]).
Regarding claim 3 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, RAFFERTY further teaches wherein the pre-seeding data comprises a predetermined set of words (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
Regarding claim 4 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 3 above, RAFFERTY further teaches wherein the predetermined set of words comprises a complete set of words permitted to be contained in the one or more words in order for the one or more words to correspond to an identification number corresponding to a valid identity (RAFFERTY on [0048, 0103, 0111-0116, 0353 and 0366-0369] teaches dictionary comprises words for generating authentication string for validation).
Regarding claim 5 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 3 above, RAFFERTY further teaches wherein configuring the speech recognition service using the pre-seeding data comprises configuring the speech recognition service to exclusively or preferentially identify words contained in the predetermined set while performing speech recognition (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies).
Regarding claim 6 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, RAFFERTY further teaches wherein obtaining the pre-seeding data comprises retrieving the pre-seeding data from a memory accessible to the at least one computing device (RAFFERTY on [0258, 0343, 0349 and 0353] teaches dictionary stored in memory accessible to user and authentication server).
Regarding claim 7 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, RAFFERTY further teaches wherein the pre-seeding data comprises identification information that identifies the user or a device of the user (RAFFERTY on [0103 and 0115-0118] teaches authentication word string for authenticating user).
Regarding claim 8 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 2 above, RAFFERTY further teaches wherein obtaining the pre-seeding data comprises receiving the pre-seeding data from the third party (RAFFERTY on [0258 and 0343] teaches receiving dictionary from authentication server).
Regarding claim 10 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, RAFFERTY further teaches further comprising determining that the automatic speech recognition result meets predetermined confidence criteria (RAFFERTY on [0036] teaches the words may be selected from a dictionary, wherein the dictionary comprises words which do not have one or more of the following characteristics: a) more than N letters (preferably, where N is 10, 9, 8, 7, 6 or 5); b) fewer than M letters (preferably, where N is 3, 2 or 1); c) are difficult to pronounce or spell; d) sound similar when pronounced to other words in the dictionary; and e) do not relate to a common theme.)
Regarding claim 11 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, RAFFERTY further teaches wherein processing the automated speech recognition result to obtain the identification number comprises processing the one or more words and an obfuscation factor (RAFFERTY on [0370-0374] teaches an authentication word string is determined according to the algorithm of the form:
AWS=E.sub.k(P)
wherein AWS=authentication word string, P=plaintext (payment details, such as details of user payment card or bank account plus the amount or Value of transaction), E=encryption method (i.e., Obfuscation factor) (used to determine word replacements for the plaintext from the dictionary authentication) k=key (seed for random number generator RNG component of algorithm i.e., also as obfuscation factor)).
Regarding claim 12 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 11 above, RAFFERTY further teaches wherein the obfuscation factor is dependent on an identity of the third party (RAFFERTY on [0259-0252] teaches key information relating to the seed keys used in the authentication word string generation algorithm. Key information (specific to the user) comprising a key ID and key table index, is retrieved from the user enrolment database. See on [0370-0377] teaches the key k may be transmitted from the user and authentication server alongside the authentication word string).
Regarding claim 13 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 11 above, RAFFERTY further teaches wherein the obfuscation factor is unique to a particular communication session between the user and the third party (RAFFERTY on [0262 and 0296] teaches generating unique key for each transaction).
Regarding claim 14 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 11 above, RAFFERTY further teaches wherein the obfuscation factor is valid for a predetermined time window (RAFFERTY on [0388] teaches time dependent key).
Regarding claim 16 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, RAFFERTY further teaches wherein processing the automated speech recognition result to obtain the identification number comprises confirming that each word in the list of words appears in a predetermined set of words stored in a memory accessible by the computing device, wherein the predetermined set of words is in compliance with one or more predefined criteria (RAFFERTY on [0036] teaches the words may be selected from a dictionary, wherein the dictionary comprises words which do not have one or more of the following characteristics: a) more than N letters (preferably, where N is 10, 9, 8, 7, 6 or 5); b) fewer than M letters (preferably, where N is 3, 2 or 1); c) are difficult to pronounce or spell; d) sound similar when pronounced to other words in the dictionary; and e) do not relate to a common theme).
Regarding claim 17 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 16 above, RAFFERTY further teaches wherein one of the predefined criteria is that the predetermined set of words lacks homophones (RAFFERTY on [0036] teaches the words may be selected from a dictionary, wherein the dictionary comprises words which do not have one or more of the following characteristics: a) more than N letters (preferably, where N is 10, 9, 8, 7, 6 or 5); b) fewer than M letters (preferably, where N is 3, 2 or 1); c) are difficult to pronounce or spell; d) sound similar when pronounced to other words in the dictionary; and e) do not relate to a common theme. See on [0334, 0340 and 0344] words with similar sounding (homophones) or similar spelt words (homographs)).
Regarding claim 18 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 16 above, RAFFERTY further teaches wherein one of the predefined criteria is that the predetermined set of words lacks homographs (RAFFERTY on [0036] teaches the words may be selected from a dictionary, wherein the dictionary comprises words which do not have one or more of the following characteristics: a) more than N letters (preferably, where N is 10, 9, 8, 7, 6 or 5); b) fewer than M letters (preferably, where N is 3, 2 or 1); c) are difficult to pronounce or spell; d) sound similar when pronounced to other words in the dictionary; and e) do not relate to a common theme. See on [0334, 0340 and 0344] words with similar sounding (homophones) or similar spelt words (homographs)).
Regarding claim 19 RAFFERTY teaches a computing device for authenticating a user to a third party, the computing device comprising a processor configured to: (RAFFERTY on [0153] teaches apparatus for authentication and authorization comprising processor);
obtaining pre-seeding data (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
configuring a speech recognition service to perform automated speech recognition using the pre-seeding data (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
receiving an input, the input comprising an automated speech recognition result of the user’s speech during a voice-based communication session between the user and the third party, the automated speech recognition result comprising one or more words and obtained using the speech recognition service (RAFFERTY Fig 1a, Fig 1b and text on [0099-0103] teaches user 12 in communication with service provider and authentication server (i.e., third-party) equipped with voice recognition for receiving word spoken by user);
processing the automated speech recognition result using a processor of the at least one computing device to obtain an identification number corresponding to the one or more words (RAFFERTY on [0099-0103] teaches processing words spoken by user and generating authentication word string (i.e., identification number));
transmitting the identification number via a communication interface of the at least one computing device, via a communications network to a third party device associated with the third party (RAFFERTY on [0099-0103] teaches transmitting the authentication word string to authentication server);
and receiving a message from the third party device via the communication interface, via the communications network, the message comprising an indication of whether or not the identification number corresponds to a valid identity (RAFFERTY on [0099-0103] teaches authentication server 16 then attempts to authenticate the user on the basis of the received authentication word string AWS by comparison with an expected word string (EWS) and, if the match is successful, sends an authentication confirmation message or service request message directly to the service provider 18 and/or via the agent 14 to confirm that the request is legitimate and that the service may be provided to the user 12).
RAFFERTY fails to teach evaluating a checksum associated with the list of words to determine whether or not the list of words corresponds to a valid identification number, however BACHMANN from analogous art teaches evaluating a checksum associated with the list of words to determine whether or not the list of words corresponds to a valid identification number (BACHMANN on [0004 and 0037-0038] teaches examples of hash algorithms include known cryptographic hash algorithms, digital signatures, checksum values and cyclic redundancy check (CRC) algorithms used to confirm proper transmission of documents. Typical hash values generated by known hash algorithms are too large to remember. To enable users to remember the number, the hash value is used to create a unique combination of a plurality of smaller numbers that are used as word indices to identify particular words in a plurality of word lists in order to select a unique combination of words to form the mnemonic. This mnemonic then provides a memorable identifier for the document that can be used to uniquely identify the particular version).
Thus, it would have been obvious to one ordinary skill in the art before the effective filing date to implement the teaching of BACHMANN into the teaching of RAFFERTY by evaluating a checksum associated with the list of words. One would be motivated to do so in order to identify particular words using checksum in a plurality of word lists in order to select a unique combination of words to form the mnemonic (BACHMANN [0004]).
Regarding claim 20 RAFFERTY teaches a computer program for authenticating a user to a third party, the computer program containing instructions that, when executed by a processor of a computing device, cause the computing device to: (RAFFERTY on [0073] teaches a computer program and a computer program product for carrying out any of the methods described herein, and/or for embodying any of the apparatus features described herein, and a computer readable medium having stored thereon a program for carrying out any of the methods);
obtaining pre-seeding data (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
configuring a speech recognition service to perform automated speech recognition using the pre-seeding data (RAFFERTY on [0102 and 0356] teaches the authentication server 16 equipped with speech or voice recognition capability in order to determine words spoken by the user. Authentication dictionaries of different sizes may be generated to provide different single-word entropies. The generation of dictionaries generally involves selection of suitable words and the factors considered for word selection may include suitability for speech recognition/generation technologies, i.e., suitable words from dictionary as pre-seed data).
receiving an input, the input comprising an automated speech recognition result of the user’s speech during a voice-based communication session between the user and the third party, the automated speech recognition result comprising one or more words and obtained using the speech recognition service (RAFFERTY Fig 1a, Fig 1b and text on [0099-0103] teaches user 12 in communication with service provider and authentication server (i.e., third-party) equipped with voice recognition for receiving word spoken by user);
processing the automated speech recognition result using a processor of the at least one computing device to obtain an identification number corresponding to the one or more words (RAFFERTY on [0099-0103] teaches processing words spoken by user and generating authentication word string (i.e., identification number));
transmitting the identification number via a communication interface of the at least one computing device, via a communications network to a third party device associated with the third party (RAFFERTY on [0099-0103] teaches transmitting the authentication word string to authentication server);
and receiving a message from the third party device via the communication interface, via the communications network, the message comprising an indication of whether or not the identification number corresponds to a valid identity (RAFFERTY on [0099-0103] teaches authentication server 16 then attempts to authenticate the user on the basis of the received authentication word string AWS by comparison with an expected word string (EWS) and, if the match is successful, sends an authentication confirmation message or service request message directly to the service provider 18 and/or via the agent 14 to confirm that the request is legitimate and that the service may be provided to the user 12).
RAFFERTY fails to teach evaluating a checksum associated with the list of words to determine whether or not the list of words corresponds to a valid identification number, however BACHMANN from analogous art teaches evaluating a checksum associated with the list of words to determine whether or not the list of words corresponds to a valid identification number (BACHMANN on [0004 and 0037-0038] teaches examples of hash algorithms include known cryptographic hash algorithms, digital signatures, checksum values and cyclic redundancy check (CRC) algorithms used to confirm proper transmission of documents. Typical hash values generated by known hash algorithms are too large to remember. To enable users to remember the number, the hash value is used to create a unique combination of a plurality of smaller numbers that are used as word indices to identify particular words in a plurality of word lists in order to select a unique combination of words to form the mnemonic. This mnemonic then provides a memorable identifier for the document that can be used to uniquely identify the particular version).
Thus, it would have been obvious to one ordinary skill in the art before the effective filing date to implement the teaching of BACHMANN into the teaching of RAFFERTY by evaluating a checksum associated with the list of words. One would be motivated to do so in order to identify particular words using checksum in a plurality of word lists in order to select a unique combination of words to form the mnemonic (BACHMANN [0004]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over RAFFERTY et al (hereinafter RAFFERTY) (US 20210081923) in view of BACHMANN et al (hereinafter BACHMANN) (US 20090313269) and further in view of Lee et al (hereinafter Lee) (US 11960582).
Regarding claim 9 the combination of RAFFERTY and BACHMANN teaches all the limitations of claim 1 above, the combination fails to teach wherein the automated speech recognition result is provided by an AI model, however Lee from analogous art teaches
wherein the automated speech recognition result is provided by an AI model (Lee on [col 1 line 30-35] teaches an artificial intelligence (AI) service on the basis of speech recognition technology. For example, the electronic device may recognize a voice command spoken by a user on the basis of speech recognition).
Thus, it would have been obvious to one ordinary skill in the art before the effective filing date to implement the teaching of Lee into the combined teaching of RAFFERTY and BACHMANN by generating automated speech recognition result using an AI model. One would be motivated to do so in order to accurately recognize voice command spoken by user on basis of speech recognition in order to perform function corresponding to the recognized voice command (Lee on [col 1 line 30-35]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOEEN KHAN whose telephone number is (571)272-3522. The examiner can normally be reached 7AM-5PM EST M-TH Alternate Fridays.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shewaye Gelagay can be reached at (571)272-4219. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOEEN KHAN/Primary Examiner, Art Unit 2436
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