DETAILED ACTION
Introduction
1. This office action is in response to Applicant’s submission filed on 10/12/2023. Claims 1-20 are pending in the application and have been examined.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
3. The drawings filed on 10/12/2023 have been accepted and considered by the Examiner.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. 11,804,218. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of patent ‘218 anticipate the instant claims as presented in the chart below. Independent claims 1 and 11 in the current App. ‘145 are anticipated by independent claims 1 and 11 in the patent ‘218. Dependent claims 2-10; and 12-20 follow likewise the similar mapping to the corresponding dependent claims 2-10; and 12-20 in the patent ‘218.
Present App. 18/486,145:
1. (Currently Amended) A computer-implemented method executed on data processing hardware of a user device that causes the data processing hardware to perform operations comprising: receiving a voice query spoken by a user; generating, using a class-based language model, a word lattice representing a candidate transcription sequence for the voice query, the candidate transcription sequence comprising a class- based symbol, the class-based symbol corresponding to a particular class; incorporating one or more class-based inserting a list of user-specific terms that belong to the particular class at a location in the word lattice that corresponds to a position of the class-based symbol in the candidate transcription sequence; and determining a transcription for the voice query that comprises a sequence of terms including one of the one or more class-based terms user-specific terms selected from the list of user-specific terms in place of the class-based symbol in the candidate transcription sequence.
2. (Currently Amended) The computer-implemented method of claim 1, wherein the class- based language model is trained by: obtaining training language sequences that include training class-based terms corresponding to the particular class; pre-processing the training language sequences by replacing the training class-based terms in the training language sequences with the class-based symbol corresponding to the particular class; and training the class-based language model on the pre-processed training language sequences.
3. (Original) The computer-implemented method of claim 1, wherein the class-based language model comprises an n-gram model.
4. (Original) The computer-implemented method of claim 1, wherein the word lattice is represented as a finite state transducer.
5. (Original) The computer-implemented method of claim 1, wherein the class-based symbol is selected from among a plurality of class-based symbols based on context data associated with the voice query, each class-based symbol of the plurality of class-based symbols corresponding to a respective different class.
6. (Original) The computer-implemented method of claim 1, wherein the class-based language model is trained on a remote server in communication with the user device.
7. (Currently Amended) The computer-implemented method of claim 1, wherein determining the transcription for the voice query comprises selecting the one of the one or more class-based user-specific terms from the list of user-specific terms in place of the class-based symbol by identifying which of the one or more class-based terms user-specific terms from the list of user- specific terms best resembles a phonetic transcription for a corresponding portion of the voice query.
8. (Currently Amended) The computer-implemented method of claim 1, wherein the operations further comprise: obtaining context data associated with the voice query; and obtaining the one or class-based terms belonging to the class list of user-specific terms belonging to the particular class based on the context data.
9. (Currently Amended) The computer-implemented method of claim 8, wherein the obtained list of class-based terms user-specific terms comprises a contact list of the user.
10. (Original) The computer-implemented method of claim 8, wherein the candidate transcription sequence comprises a word lattice.
11. (Currently Amended) A system comprising: data processing hardware of a user device; and memory hardware in communication with the data processing hardware and storing instructions, that when executed by the data processing hardware, cause the data processing hardware to perform one or more operations comprising: receiving a voice query spoken by a user; generating, using a class-based language model, a word lattice representing a candidate transcription sequence for the voice query, the candidate transcription sequence comprising a class-based symbol, the class-based symbol corresponding to a particular class; incorporating one or more class-based inserting a list of user-specific terms that belong to the particular class at a location in the word lattice that corresponds to a position of the class-based symbol in the candidate transcription sequence; and determining a transcription for the voice query that comprises a sequence of terms including one of the one or more class-based terms user-specific terms selected from the list of user- specific terms in place of the class-based symbol in the candidate transcription sequence.
12. (Currently Amended) The system of claim 11, wherein the class-based language model is trained by: obtaining training language sequences that include training class-based terms corresponding to the particular class; pre-processing the training language sequences by replacing the training class-based terms in the training language sequences with the class-based symbol corresponding to the particular class; and training the class-based language model on the pre-processed training language sequences.
13. (Original) The system of claim 11, wherein the class-based language model comprises an n- gram model.
14. (Original) The system of claim 11, wherein the word lattice is represented as a finite state transducer.
15. (Original) The system of claim 11, wherein the class-based symbol is selected from among a plurality of class-based symbols based on context data associated with the voice query, each class- based symbol of the plurality of class-based symbols corresponding to a respective different class.
16. (Original) The system of claim 11, wherein the class-based language model is trained on a remote server in communication with the user device. 17. (Currently Amended) The system of claim 11, wherein determining the transcription for the voice query comprises selecting the one of the one or more class-based user-specific terms from the list of user-specific terms in place of the class-based symbol by identifying which of the one or more class-based terms user-specific terms from the list of user-specific terms best resembles a phonetic transcription for a corresponding portion of the voice query.
18. (Currently Amended) The system of claim 11, wherein the operations further comprise: obtaining context data associated with the voice query; and obtaining the one or class-based terms belonging to the class list of user-specific terms belonging to the particular class based on the context data.
19. (Currently Amended) The system of claim 18, wherein the obtained list of class-based terms user-specific terms comprises a contact list of the user.
20. (Original) The system of claim 18, wherein the candidate transcription sequence comprises a word lattice.
U.S. Patent 11,804,218:
1. A computer-implemented method when executed on data processing hardware of a user device causes the data processing hardware to perform operations comprising: receiving a voice query spoken by a user; obtaining a class-based language model that indicates probabilities of language sequences that include at least one class-based term belonging to a particular class, the class-based language model trained on training language sequences that include a class-based symbol in place of class-based terms that initially occurred in the training language sequences, the class-based terms corresponding to the particular class; generating, using the class-based language model, a word lattice representing a candidate transcription sequence for the voice query, the candidate transcription sequence comprising the class-based symbol; updating the class-based language model by inserting a list of user-specific terms that belong to the particular class at a location in the word lattice that corresponds to a position of the class-based symbol in the candidate transcription sequence; and generating, using the updated class-based language model, a transcription for the voice query that comprises a sequence of terms including one of the user-specific terms selected from the list of user-specific terms in pace of the class-based symbol.
2. The computer-implemented method of claim 1, wherein the class-based language model is trained by: obtaining the training language sequences that include the class-based terms corresponding to the particular class; pre-processing the training language sequences by replacing the class-based terms in the training language sequences with the class-based symbol corresponding to the particular class; and training the class-based language model on the pre-processed training language sequences.
3. The computer-implemented method of claim 1, wherein the class-based language model comprises an n-gram model.
4. The computer-implemented method of claim 1, wherein the word lattice is represented as a finite state transducer.
5. The computer-implemented method of claim 1, wherein the class-based symbol is selected from among a plurality of class-based symbols based on context data associated with the voice query, each class-based symbol of the plurality of class-based symbols corresponding to a respective different class.
6. The computer-implemented method of claim 1, wherein the class-based language model is trained on a remote server in communication with the user device.
7. The computer-implemented method of claim 1, wherein generating the transcription for the voice query comprises selecting the one of the user-specific terms from the list of user-specific terms in place of the class-based symbol by identifying which of the user-specific terms from the list of user-specific terms best resembles a phonetic transcription for a corresponding portion of the voice query.
8. The computer-implemented method of claim 1, wherein the operations further comprise: obtaining context data associated with the voice query; and obtaining the list of user-specific terms belonging to the particular class based on the context data.
9. The computer-implemented method of claim 8, wherein the obtained list of user- specific terms comprises a contact list of the user.
10. The computer-implemented method of claim 8, wherein the candidate transcription sequence comprises a word lattice.
11. A system comprising: data processing hardware of a user device; and memory hardware in communication with the data processing hardware and storing instructions, that when executed by the data processing hardware, cause the data processing hardware to perform one or more operations comprising: receiving a voice query spoken by a user; obtaining a class-based language model that indicates probabilities of language sequences that include at least one class-based term belonging to a particular class, the class-based language model trained on training language sequences that include a class-based symbol in place of class-based terms that initially occurred in the training language sequences, the class-based terms corresponding to the particular class; generating, using the class-based language model, a word lattice representing a candidate transcription sequence for the voice query, the candidate transcription sequence comprising the class-based symbol; updating the class-based language model by inserting a list of user- specific terms that belong to the particular class at a location in the word lattice that corresponds to a position of the class-based symbol in the candidate transcription sequence; and generating, using the updated class-based language model, a transcription for the voice query that comprises a sequence of terms including one of the user-specific terms selected from the list of user-specific terms in pace of the class- based symbol.
12. The system of claim 11, wherein the class-based language model is trained by: obtaining the training language sequences that include the class-based terms corresponding to the particular class; pre-processing the training language sequences by replacing the class-based terms in the training language sequences with the class-based symbol corresponding to the particular class; and training the class-based language model on the pre-processed training language sequences.
13. The system of claim 11, wherein the class-based language model comprises an n-gram model.
14. The system of claim 11, wherein the word lattice is represented as a finite state transducer.
15. The system of claim 11, wherein the class-based symbol is selected from among a plurality of class-based symbols based on context data associated with the voice query, each class-based symbol of the plurality of class-based symbols corresponding to a respective different class.
16. The system of claim 11, wherein the class-based language model is trained on a remote server in communication with the user device.
17. The system of claim 11, wherein generating the transcription for the voice query comprises selecting the one of the user-specific terms from the list of user-specific terms in place of the class-based symbol by identifying which of the user-specific terms from the list of user-specific terms best resembles a phonetic transcription for a corresponding portion of the voice query.
18. The system of claim 11, wherein the operations further comprise: obtaining context data associated with the voice query; and obtaining the list of user-specific terms belonging to the particular class based on the context data.
19. The system of claim 18, wherein the obtained list of user-specific terms comprises a contact list of the user.
20. The system of claim 18, wherein the candidate transcription sequence comprises a word lattice.
Allowable Subject Matter
5. Claims 1-20 would be allowable over the prior art of record.
6. The following is an Examiner’s Statement of Reasons for Allowance
As per independent claims 1 and 11, Arnold et al., (U.S. Patent Application Publication: 2003/0182131), already of record, hereinafter referred to as ARNOLD, teaches see e.g., a class-based statistical language model, which uses N-grams computed over words, classes, and a special “catch-all” word model. ARNOLD also discloses see e.g., Grammar Manager 132 is accessed to provide the necessary downloads (e.g., a new grammar) to address the detected change in topic or intent… language model (LM) of the background speech recognizer 120b is a model that is formed with the words that most usefully differentiate the individual spoken language applications with additional filler models to catch all other non-differentiating words. And furthermore, ARNOLD discloses, see e.g., language model (LM) of the background speech recognizer 120b is a model that is formed with the words that most usefully differentiate the individual spoken language applications with additional filler models to catch all other non-differentiating words where the language model is a class-based statistical language model, which uses N-grams computed over words, classes, and a special “catch-all” word model, sequence of out of vocabulary words mapped to the “catch all” words can be converted to a single “catch all” instance, thereby allowing the statistical language model to capture long term dependencies when a sequence of relevant words is intermixed with a sequence of “catch all” words, (See e.g., ARNOLD ¶¶64-72).
Notwithstanding ARNOLD’s teachings, still fail to teach or fairly suggest either individually or in a reasonable combination the recited limitations in independent claims 1 and 11 as specifically claimed.
Similarly, dependent claims 2-10; and 12-20 further limit allowable independent claims 1 and 11, correspondingly, and thus they would also be allowable over the prior art of record by virtue of their dependency.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lee et al., (L. -s. Lee, J. Glass, H. -y. Lee and C. -a. Chan, “Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 9, pp. 1389-1420, Sept. 2015), discloses see e.g., how “…framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences…” (See e.g., Lee et al., Abstract, Figs. 1, 2, and 3).
Please, see PTO-892 for more details.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Edgar Guerra-Erazo whose telephone number is (571) 270-3708. The examiner can normally be reached on M-F 7:30a.m.-5:00p.m. EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bhavesh Mehta can be reached on (571) 272-7453. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/EDGAR X GUERRA-ERAZO/Primary Examiner, Art Unit 2656