Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Arguments
Applicant's arguments filed 02/03/2026 have been fully considered but they are not persuasive.
On pages 8-9 of the arguments:
Applicant quotes an embodiment not cited in the office (e.g. 0039 was not cited), thereby arguing that such an embodiment invalidates the pertinent embodiments (e.g. at least 0004 and 0037), such that the art (Kang) fails to teach:
“the contextual language model comprises medical terminology that is not included in a vocabulary used to train the pre-trained ASR system”
Examiner does not concur, the office action expressly cites 0004 and 0037:
A language model that has an OOV scheme is expressly taught and a context-specific instance such as medical, i.e. terminology which lacks presence included in the vocabulary of the model. The OOV is the vocabulary not included by its very definition. The specification 0050 and 0056 of the present invention aligns with such a concept taught in the prior art of Kang.
Further amendment is strongly suggested.
Regarding 35 USC 101:
In view of the recent guidance via the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, the 35 USC 101 rejection has been withdrawn, see rationale below.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve context-based language models in ASR which use OOV in training, supported by the specification, and reflected by the claims e.g. in spec: 0025, 0037, 0044, In other words, the claims enable the invention to perform more accurate ASR in a specific context to increase the likelihood of predicting the context specific terminology in the spoken context-specific speech, to decrease the word error rate in said context-specific setting without the onerous process of collecting large amounts of said context-specific speech and training the ASR system thereof.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-8, 11, 14-16, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210343274 A1 Kang; Young Mo et al. (hereinafter Kang).
Re claim 1,
Re claim 1, Kang teaches
1. A method of generating text of medical speech, the method comprising: (0004)
providing a pre-trained automatic speech recognition (ASR) system stored in memory and executed on a processor; (ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context based language models with n-gram biased multi-score analysis, including beam options 0022 & 0037)
receiving, by the pre-trained ASR system, spoken medical speech; and (ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context based language models with n-gram biased multi-score analysis, including beam options 0022 & 0037)
generating text of the spoken medical speech by biasing the pre-trained ASR system using a contextual language model, wherein the contextual language model comprises medical terminology that is not included in a vocabulary used to train the pre-trained ASR system. (ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context based language models with n-gram biased multi-score analysis, including beam options 0022 & 0037)
Re claim 11, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
Re claim 16, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
Re claim 5, Kang teaches
5. The method of claim 1, wherein the medical terminology comprises a plurality of medical terms. (0004)
Re claims 6 and 19, Kang teaches
6. The method of claim 1, wherein the contextual language model is a contextual n-gram language model. (ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context based language models with n-gram biased multi-score analysis, including beam options 0022 & 0037)
Re claim 7, Kang teaches
7. The method of claim 6, wherein the step of generating text of the spoken medical speech comprises determining an n-gram score based on an overall model score generated by the pre-trained ASR system and a bias score generated by the contextual language model to generate a textual representation of a medical term that is not included in the vocabulary used to train the pre-trained ASR system. (ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context-based language models with n-gram biased multi-score analysis e.g. bias score + base score as in claim 1 with 0028-0029, including beam options 0022 & 0037)
Re claims 8 and 15, Kang teaches
8. The method of claim 1, wherein the contextual language model biases the ASR system during beam searching. (operations taking place during a decoding beam, ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context based language models with n-gram biased multi-score analysis, including beam options 0022 & 0037)
Re claims 14 and 20, Kang teaches
14. The system of claim 12, wherein generating text of the spoken medical speech comprises determining an n-gram score based on an overall model score generated by the pre-trained ASR system and a bias score generated by the contextual language model. (ASR system trained prior 0026 to address terms such as medical terms 0004 that are out of vocabulary or OOV using class-based or context-based language models with n-gram biased multi-score analysis e.g. bias score + base score as in claim 1 with 0028-0029, including beam options 0022 & 0037)
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 2-4, 12, 13, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210343274 A1 Kang; Young Mo et al. (hereinafter Kang) in view of US 20200027444 A1 Prabhavalkar; Rohit Prakash et al. (hereinafter Prabhavalkar).
Re claims 2, 12, and 17, Kang teaches an acoustic model and multiple language models with utilization of pronunciation training, but fails to teach a pronunciation model as follows:
2. The method of claim 1, wherein the pre-trained ASR system comprises an acoustic model, a pronunciation model, and a language model that have been jointly trained using the vocabulary. (s2s embodiment concepts for separate training 0066, joint training embodiment 0115, and embodiments in context such as shallow fusions)
Therefore, 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 system of Kang to incorporate the above claim limitations as taught by Prabhavalkar to allow for a simple substitution of one known element, such as Prabhavalkar’s common ASR system containing a pronunciation model in addition to an AM and LM based on a language/vocabulary, for another such as Kang’s AM + LM scheme, to obtain predictable results such as sequence-2-sequence modeling single or joint concept in s2s for strong pre-trained and post-trained models as well as improved accuracy using such external models weighting whether joint or separate models are ideal using the language model as the driving factor i.e. based on the language in both scenarios handling multi-domain ASR tasks.
Re claims 3, 13, and 18, Kang teaches an acoustic model and multiple language models with utilization of pronunciation training, but fails to teach a pronunciation model as follows:
3. The method of claim 1, wherein the pre-trained ASR system comprises an acoustic model, a pronunciation model, and a language model that have been separately trained, and wherein the language model is trained using the vocabulary. (s2s embodiment concepts for separate training 0066, joint training embodiment 0115, and embodiments in context such as shallow fusions)
Therefore, 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 system of Kang to incorporate the above claim limitations as taught by Prabhavalkar to allow for a simple substitution of one known element, such as Prabhavalkar’s common ASR system containing a pronunciation model in addition to an AM and LM based on a language/vocabulary, for another such as Kang’s AM + LM scheme, to obtain predictable results such as sequence-2-sequence modeling single or joint concept in s2s for strong pre-trained and post-trained models as well as improved accuracy using such external models weighting whether joint or separate models are ideal using the language model as the driving factor i.e. based on the language in both scenarios handling multi-domain ASR tasks.
Re claim 4, teaches an acoustic model and multiple language models with utilization of pronunciation training, but fails to teach a pronunciation model as follows:
4. The method of claim 1, wherein the biased ASR system is a shallow fusion model. (s2s embodiment concepts for separate training 0066, joint training embodiment 0115, and embodiments in context such as shallow fusions)
Therefore, 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 system of Kang to incorporate the above claim limitations as taught by Prabhavalkar to allow for a simple substitution of one known element, such as Prabhavalkar’s common ASR system containing a pronunciation model in addition to an AM and LM based on a language/vocabulary, for another such as Kang’s AM + LM scheme, to obtain predictable results such as sequence-2-sequence modeling single or joint concept in s2s for strong pre-trained and post-trained models as well as improved accuracy using such external models weighting whether joint or separate models are ideal using the language model as the driving factor i.e. based on the language in both scenarios handling multi-domain ASR tasks.
Claim 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210343274 A1 Kang; Young Mo et al. (hereinafter Kang) in view of US 12198681 B1 Sunkara; Monica Lakshmi (hereinafter Sunkara).
Re claim 9, teaches an acoustic model and multiple language models with utilization of pronunciation training, but fails to teach a pronunciation model as follows:
9. The method of claim 1, wherein the contextual language model biases the ASR system before beam searching. (Sunkara col 12 lines 52-61)
Therefore, 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 system of Kang to incorporate the above claim limitations as taught by Sunkara to allow for a simple substation of one known element, such as beam forming prior to biasing, for another, such as Kang’s existing beam searching/decoding during operations, to obtain predictable results for n-gram boosting where OOV words as added to the external language model with a fixed high unigram probability to achieve biasing.
Claims 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210343274 A1 Kang; Young Mo et al. (hereinafter Kang) in view of US 20230315815 A1 GANONG, III; William F. et al. (hereinafter Ganong).
Re claim 10, Kang teaches medical terms and results generated thereof, but fails to teach:
10. A method of generating a medical report, comprising: the method of claim 1; and, writing a report based on the text of the medical speech. (Ganong 0025)
Therefore, 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 system of Kang to incorporate the above claim limitations as taught by Ganong to allow for simple substitution of one known element such as transcription in a medical context e.g. a report, for another such as Kang’s general embodiment of medical results, to obtain predictable results such as in-context per industry e.g. instead of search results or a summary or a browser output, a medical report is deliver via ASR.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12437756 B2 Aleksic; Petar et al.
OOV concepts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov