Detailed Action
This office action is in response to applicant’s submission filed on July 11, 2025.
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 .
Drawings
The drawings filed on July 11, 2025 have been accepted. The drawings were searched for informalities.
Specification
The specification filed on July 11, 2025 has been accepted. The specification was searched for informalities.
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.
Claims 1-2, 5, 8-9, 12, 15-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0193009 A1 to Shafet et al. (hereinafter, “Shafet”) in view of US 2003/0163739 A1 to Armington et al. (hereinafter, “Armington”) and in further view of US 2009/0132237 A1 to Gugenheim.
Regarding claim 1, Shafet discloses: A non-transitory computer-readable medium comprising
instructions which, when executed by one or more hardware processors, cause performance of operations comprising (“Computing device 110, service provider server 130, and external text resource 150 may each include one or more processors, memories, and other appropriate components for executing instructions such as program code and/or data stored on one or more computer readable mediums to implement the various applications, data, and steps described herein” [0026]):
obtaining a set of candidate words from one or more reference sources (“The input text data for generation of the queries may correspond to a corpus of documents available to the system, such as an online resource. For example, an online encyclopedia may provide online documents that may be parsed for text strings, sentences, paragraphs, and the like, which may be used as the input text data for query generation. Other online resources, such as messaging boards, help resources, news websites, etc. may also provide sample input text. The text data may also come from a media viewing website (e.g., NETFLIX®), such as content from a television show” [0014]);
generating a plurality of custom dictionaries using the set of candidate words, wherein generating a custom dictionary of the plurality of custom dictionaries comprises (“The system may also generate a list of acceptable responses or answers to fill in the missing word(s) within the text string. The list may include the removed word, as well as other words that satisfy the query” [0017] [Examiner notes that the custom lists are seen as the custom dictionaries]):
selecting a subset of words from the set of candidate words based on at least one selection criterion corresponding to the custom dictionary (““Key” words may be identified by determining which word(s) in the sentence have the most plausible answers or substitutions and using the top one, two, three, or other predetermined number” [0017]);
generating, for individual words in the subset of words, respective feature vectors representing To select the word(s) for removal and determine one or more acceptable answers or responses for entry to the text string to satisfy the query, the system may first perform a language modeling and feature learning process to understand the semantic and syntax of the sentence, for example, by generating word embeddings in vector space of the words of the sentence, thereby understanding the relationship of the words within the text string to the other words. For example, using a process such as Word2vec, word vectors for words in a sentence may be generated, and a machine learning process may be trained that identifies sample words in a sentence for removal and allowable word responses or answers for entry by a user in the removed or missing word's place” [0016]); and
based on the respective feature vectors for the individual words in the subset of words, ordering the subset of words to generate an ordered subset of words (“The system may also score or determine a probability of entering each of the alternative words, which may be based on an occurrence rate of the words in the input text data and/or a distance between the word vectors” [0017]);
including the ordered subset of words in the custom dictionary of the plurality of custom dictionaries (“The list may include the removed word, as well as other words that satisfy the query. The list of acceptable responses may be based on similar word vectors as the removed word” [0017]);
wherein the user-authentication application authenticates a user based on the ordered subset of words in the custom dictionary of the plurality of custom dictionaries (“When receiving entry of one or more words to fill the missing words in the text string, the system may then determine whether the device's entry is within or satisfies the list of acceptable answers. Further if a probability score or threshold is instituted by the system, the system may require that the device's entry is above the threshold probability/score required for the authentication query... If the device's entry of words is acceptable, the device may then be authenticated as being used by a person, and the device may be allowed to access or use the service provider's processes, data, or services” [0020-0021]).
Shafet does not disclose: generating, for individual words in the subset of words, respective feature vectors representing phonetic encodings of the individual words; and
However, Armington discloses: generating, for individual words in the subset of words, respective feature vectors representing phonetic encodings of the individual words (“For example, the user's voice may be processed by a speech sampling and noise-filtering algorithm, which breaks down the voice into phonemes to be stored in a voice template file” [0082]); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet with the added structure of Armington in order for the system to be able to efficiently read phonetic encodings of words along with the individual words.
Shafet does not disclose: categorizing the plurality of custom dictionaries based on the corresponding selection criteria; and storing the plurality of custom dictionaries as categorized data structures for use by a user- authentication application,
However, Gugenheim discloses: categorizing the plurality of custom dictionaries based on the corresponding selection criteria (“The first criterion for the construction of orthogonal dictionaries, according to an embodiment of the present invention, is to maximize the phonetic distance between all the pairs of words of the dictionary. Another preferable criterion is “balance” or distributing the number of words in the vocabulary substantially equally among the channels. Reference is now made to FIG. 3 which illustrates schematically a method 30 for distributing a target vocabulary 301 into multiple orthogonal dictionaries. In order to distribute target vocabulary 301 into multiple orthogonal dictionaries, vocabulary are first categorized into groups of minimal phonetic distance between the members of each group, the categorization is performed, according to different embodiments of the present invention by applying the following steps, preferably in the order presented: 303 Categorization by Phonetic Length...305 Categorization by Vowel Combinations... 307 Categorization by Consonant Placement” [0043-0048]); and
storing the plurality of custom dictionaries as categorized data structures for use by a user- authentication application (“The vocabulary and the dictionaries are stored in memory operatively attached to a computer system” [0017]; “According to the present invention there is provided a computer readable medium readable by a machine, tangibly storing the multiple dictionaries produced by the methods as disclosed herein” [0020]),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet with the added structure of Gugenheim in order for the system to be able to efficiently organize the lists of words into categories.
Claim 8 recites substantially the same limitation as claim 1, in the form of a method for implementing the corresponding system, therefore it is rejected under the same rationale.
Claim 15 recites substantially the same limitation as claim 1, in the form of a system for implementing the corresponding system, therefore it is rejected under the same rationale.
Regarding claims 2, 9, and 16, a combination of Shafet-Armington-Gugenheim disclose the system of claims 1/8/15.
Shafet does not disclose: wherein: a first selection criterion corresponding to a first custom dictionary of the plurality of custom dictionaries comprises inclusion in a first category of words belonging to a first part of speech; a second selection criterion corresponding to a second custom dictionary of the plurality of custom dictionaries comprises inclusion in a second category of words belonging to a second part of speech; and the first part of speech is different than the second part of speech.
However, Gugenheim discloses: wherein: a first selection criterion corresponding to a first custom dictionary of the plurality of custom dictionaries comprises inclusion in a first category of words belonging to a first part of speech; a second selection criterion corresponding to a second custom dictionary of the plurality of custom dictionaries comprises inclusion in a second category of words belonging to a second part of speech; and the first part of speech is different than the second part of speech (“The first criterion for the construction of orthogonal dictionaries, according to an embodiment of the present invention, is to maximize the phonetic distance between all the pairs of words of the dictionary. Another preferable criterion is “balance” or distributing the number of words in the vocabulary substantially equally among the channels. Reference is now made to FIG. 3 which illustrates schematically a method 30 for distributing a target vocabulary 301 into multiple orthogonal dictionaries. In order to distribute target vocabulary 301 into multiple orthogonal dictionaries, vocabulary are first categorized into groups of minimal phonetic distance between the members of each group, the categorization is performed, according to different embodiments of the present invention by applying the following steps, preferably in the order presented: 303 Categorization by Phonetic Length...305 Categorization by Vowel Combinations... 307 Categorization by Consonant Placement” [0043-0048]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet with the added structure of Gugenheim in order for the system to be able to efficiently organize the lists of words into categories.
Regarding claims 5, 12, and 19, a combination of Shafet-Armington-Gugenheim disclose the system of claims 1/8/15.
Shafet does not disclose: wherein the generating the feature vectors comprises: applying a phonetic encoding algorithm to the individual words in the subset of words.
However, Gugenheim discloses: wherein the generating the feature vectors comprises: applying a phonetic encoding algorithm to the individual words in the subset of words (“Furthermore, since the words in each channel have been selected, according to an embodiment of the present invention for orthogonality, that is to have a large phonetic distance from each other, an even higher confidence level may be achieved or in a different design a faster or simpler speech recognition algorithm may be used for the channels than would be required without distributing orthogonally into separate dictionaries according to the teachings of the present invention. Many speech recognition algorithms are known. One class of commonly used algorithms are based on hidden Markov models (HMM). The speech recognition algorithm for use with embodiments of the present invention may be of any such mechanisms known in the art” [0035-0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet with the added structure of Gugenheim in order for the system to be able to efficiently apply a phonetic algorithm to the words into categories.
Claims 3-4, 10-11, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0193009 A1 to Shafet et al. (hereinafter, “Shafet”) in view of US 2003/0163739 A1 to Armington et al. (hereinafter, “Armington”) and in further view of US 2009/0132237 A1 to Gugenheim and in further view of US 9606983 B1 to McClintock et al. (hereinafter, “McClintock”).
Regarding claims 3, 10, and 17, a combination of Shafet-Armington-Gugenheim disclose the system of claims 2/9/16.
Shafet-Armington-Gugenheim do not disclose: wherein: the first part of speech comprises verbs or adjectives; and the second part of speech comprises nouns.
However, McClintock discloses: wherein: the first part of speech comprises verbs or adjectives; and the second part of speech comprises nouns (“As illustrated by FIG. 1, the dictionary 102 contains 4,000 nouns 104, 32 verbs 106 and 16 adjectives 108” [Col. 3, lines 40-41]; “For example, the computer system may separate the words into corresponding elements of speech such as noun or verbs. For the purposes of the present disclosure, the various elements of speech, such as nouns and verbs, may be considered separate categories of words. For example, a set of nouns may be considered a separate category of words from a set of adjectives” [Col. 5, lines 34-40] [Examiner notes that this identified verbs and adjectives being separate from nouns in word categories]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet-Armington-Gugenheim with the added structure of McClintock in order for the system to be able to efficiently organize the lists of words into speech categories.
Regarding claims 4, 11, and 18, a combination of Shafet-Armington-Gugenheim disclose the system of claims 1/8/15.
Shafet does not disclose: wherein the at least one selection criterion comprises one or more of the following: inclusion in a category of words having a particular word form; inclusion in a category of words limited to a maximum character length; and inclusion in a category of words comprising proper nouns, conjunctions, prepositions, adverbs, contractions, and interjections.
However, Gugenheim discloses: wherein the at least one selection criterion comprises one or more of the following: inclusion in a category of words having a particular word form (“In order to distribute target vocabulary 301 into multiple orthogonal dictionaries, vocabulary are first categorized into groups of minimal phonetic distance between the members of each group, the categorization is performed, according to different embodiments of the present invention by applying the following steps, preferably in the order presented: 303 Categorization by Phonetic Length...305 Categorization by Vowel Combinations... 307 Categorization by Consonant Placement” [0043-0048]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet with the added structure of Gugenheim in order for the system to be able to efficiently organize the lists of words into categories.
Gugenheim does not disclose: inclusion in a category of words limited to a maximum character length; and inclusion in a category of words comprising proper nouns, conjunctions, prepositions, adverbs, contractions, and interjections.
However, McClintock discloses: inclusion in a category of words limited to a maximum character length (“The dictionary 102 may also be a curated dictionary such that the words included in the dictionary are selected based at least in part on one or more various factors including length, difficulty to read, difficulty to pronounce, grade level or other factors suitable for discriminating between words that may be included in the dictionary 102” [Col. 4, lines 57-62]); and
inclusion in a category of words comprising proper nouns, conjunctions, prepositions, adverbs, contractions, and interjections (“In some embodiments, the dictionary may include other elements of speech such as verbs, adverbs, prepositions, pronouns and other elements of speech suitable for generating symbols representative of binary or hexadecimal sequences” [Col. 2, lines 39-43]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet-Armington-Gugenheim with the added structure of McClintock in order for the system to be able to efficiently organize the lists of words into more categories.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0193009 A1 to Shafet et al. (hereinafter, “Shafet”) in view of US 2003/0163739 A1 to Armington et al. (hereinafter, “Armington”) and in further view of US 2009/0132237 A1 to Gugenheim and in further view of US 9606983 B1 to McClintock et al. (hereinafter, “McClintock”) and in further view of US 11593085 B1 to Khare et al. (hereinafter, “Khare”).
Regarding claims 6, 13, 20, a combination of Shafet-Armington-Gugenheim disclose the system of claims 1/8/15.
Shafet further discloses: wherein: the feature vectors comprise alphanumeric values (“Thus, speech recognition module 240 converts incoming spoken words to alphanumeric strings (or other textual forms as appropriate to non-alphabet-based languages), typically based on a universal speaker model (i.e., not specific to a particular person) for a given language” [0025]); and
Shafet does not disclose: sorting the subset of words comprises ordering individual words of the subset of words based on the alphanumeric values of the respective feature vectors.
However, McClintock discloses: sorting the subset of words comprises ordering individual words of the subset of words based on The computer system may order or otherwise organize the words included in the dictionary before generating the symbol table 202.... Additionally, the computer system may place the words in alphabetical order, in order by length or some other order suitable for generating the symbol table 202” [Col. 5, lines 32-43]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet-Armington-Gugenheim with the added structure of McClintock in order for the system to be able to efficiently order the subset of words.
McClintock does not disclose: the alphanumeric values
However, Khare discloses: the alphanumeric values (“Moreover, the .reg file will be generated so that the key-value pairs are sorted in a deterministic order, for example, in an alphanumeric order of the keys” [Col. 15, lines 61-63])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet-Armington-Gugenheim-McClintock with the added structure of Khare in order for the system to be able to efficiently order the subset of words that can include the alphanumeric values.
Claims 7, 14, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0193009 A1 to Shafet et al. (hereinafter, “Shafet”) in view of US 2003/0163739 A1 to Armington et al. (hereinafter, “Armington”) and in further view of US 2009/0132237 A1 to Gugenheim and in further view of US 9606983 B1 to McClintock et al. (hereinafter, “McClintock”) and in further view of US 20180018577 A1 to Fink et al. (hereinafter, “Fink”).
Regarding claims 7, 14, 21, a combination of Shafet-Armington-Gugenheim disclose the system of claims 1/8/15.
Shafet does not disclose: wherein the operations further comprise: selecting at least two dictionaries of the plurality of custom dictionaries; and randomly selecting a word from the at least two dictionaries based on the respective feature vectors; and generating, by the user-authentication application, an authentication code to be spoken by a user by combining the selected words into a phrase.
However, McClintock discloses: generating, by the user-authentication application, an authentication code to be Additionally, authentication information may be represented using one or more symbols generated from a dictionary of words” [Col. 2, lines 30-32]; “Additionally, the symbols may include any number of dictionary elements, for example, a symbol may include an adjective-noun-verb set such as ‘red dog run,’ ‘smart cow sleep’ and ‘blue whale eat’” [Col. 2, lines 43-46]; “The set of word pairs may include 4 adjective-noun pairs representing a 64-bit time-limited unique authenticator for the customer” [Col. 2, lines 36-38]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet with the added structure of McClintock in order for the system to be able to securely authenticate the user.
McClintock does not disclose: generating, by the user-authentication application, an authentication code to be spoken by a user by combining the selected words into a phrase.
However, Gugenheim discloses: generating, by the user-authentication application, an authentication code to be spoken by a user by combining the selected words into a phrase (“In another aspect of an exemplary embodiment of the multi-factor authentication process, the authentication subsystem 120 also prompts the user to speak or otherwise input a secure passcode (e.g., an OTP) (more generally, a second authentication factor) via the portal subsystem 200. Just as with the user's claimed identity, the secure passcode may be provided directly (e.g., as an alphanumeric string), or via voice input” [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet-McClintock with the added structure of Gugenheim in order for the system to be able to securely authenticate the user with a code that could be spoken.
Shafet-Armington-Gugenheim-McClintock do not disclose: wherein the operations further comprise: selecting at least two dictionaries of the plurality of custom dictionaries; and randomly selecting a word from the at least two dictionaries based on the respective feature vectors; and
However, Fink discloses: wherein the operations further comprise: selecting at least two dictionaries of the plurality of custom dictionaries; and randomly selecting a word from the at least two dictionaries based on the respective feature vectors (“receiving at least two dictionaries...selecting at least one word at random from at least one of the at least two dictionaries” [0003]); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the roaming payment system of Shafet-Armington-Gugenheim-McClintock with the added structure of Gugenheim in order for the system to be able to adhere to a random selection feature configuration.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Takeda (US 8706484 B2) teaches techniques for a voice recognition dictionary generation apparatus and method for suppressing reduction of processing speed at the time of updating. The apparatus includes an input unit configured to receive a text subjected to voice recognition, a storage unit configured to store the text with respect to each file of a predetermined item, a reading data generation unit configured to analyze the text and generate a reading data, and a voice recognition dictionary configured to include content dictionaries that store therein the reading data of the text with respect to each file of the predetermined item. When the file of the predetermined item including the text stored in the storage unit is updated, a control unit detects a total number of the content dictionaries, and when the total number is smaller than a predetermined limit, the control unit generates the content dictionaries with respect to each updated predetermined item.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARON MATTHEWOS WORKU whose telephone number is (703)756-1761. The examiner can normally be reached Monday - Friday, 9:30 am - 6:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Linglan Edwards can be reached on 571-270-5440. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SARON MATTHEWOS WORKU/Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408
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