DETAILED ACTION
This communication is in response to the Response to Election filed on 7/15/2026. Claims 1-7, and 14-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant claims the benefit of US Provisional Application No. 63/539,565, filed September 9, 2023. Claims 1-7 and 14-20 have been afforded the benefit of this filing date.
Election/Restrictions
Claims 8-13 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected speech to text systems, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 7/15/2026.
Applicant's election with traverse of Group 1 in the reply filed on 7/15/2026 is acknowledged. The traversal is on the ground(s) that searching all the claims of the present application would not place a serious burden on the Examiner as it is not clear how searching for the elected group would not also result in searching for the non-elected group. This is not found persuasive because as explained in the previous action these sets are performing different tasks. The elected claims involve searching for methods of generating similarity matrices in order to further train encoders. The non-elected claims involve receiving user input to select class vectors and perform device operations. These topics would each require separate searches as the claims have little overlap and are not dependent on each other.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 and 17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 and 17 recites the limitation "the similarity scores". There is insufficient antecedent basis for this limitation in the claim. The claim element is not introduced in claims 4 and 17 nor in dependency claims 1 and 14.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 14-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 14 recite A method performed by at least one [processor], the method comprising: receiving one or more training text sentences; generating one or more training vectors based on inputting the one or more training sentences input into a text encoder, the one or more training vectors corresponding to one or more operations that an electronic device is configured to perform; generating one or more speech vectors based on one or more speech utterances input into a speech encoder; generating a similarity matrix that compares each of the one or more training vectors with each of the one or more speech vectors; and updating at least one of the text encoder and the speech encoder based on the similarity matrix.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of performing the method as it is primarily about organizing data. A human can receive sentences and utterances that correspond to operations. They can then encode them by converting them into a numerical representation such as ASCII. They could then be compared to each other to calculate a similarity and that similarity could be organized into a 3D array representing a similarity matrix. They could then make the design decision to use the information in this array as training data for encoders. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite the additional component of a processor. The processor is merely used to apply the mental process via a generic computing device. The processor is described in paragraph 49 of the specification with a generic description of the component. Claim 14 specifically lists the additional component of a memory. The memory is merely used to apply the mental process via a generic computing device. The memory is described in paragraph 49 of the specification with a generic description of the component. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 2 and 15 recite wherein the one or more training text sentences are received from a supervised dataset that labels each text sentence from the one or more text sentences with a label corresponding to an operation from the one or more operations.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can read an example set of sentences that are labelled with operations and use that information to interpret alternative language that could also correspond to the same operations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional components that were not present in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 3 and 16 recite wherein the similarity matrix comprises comparing each training vector from the one or more training vectors with each speech vector from the one or more speech vectors by determining a similarity score between a respective training vector and a respective speech vector that indicates a degree of similarity between the respective training vector and the respective speech vector.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can determine a measure of similarity between two sentences ans store this value in a 2D array (matrix). This could be done on vectorized versions of the sentences using some sort of mathematical algorithm or just comparing the numerical values directly. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional components that were not present in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 4 and 17 recite wherein a sum of each of the similarity scores in each column of the similarity matrix is 1.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of constructing a 3D array where the columns add to 1. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional components that were not present in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 5 and 18 recite wherein each diagonal entry in the similarity matrix has a higher similarity score than a non-diagonal entry.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of constructing a 3D array where the diagonals have a higher similarity score than the non-diagonals. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional components that were not present in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 6 and 19 recite wherein at least one non-diagonal entry in the similarity matrix has a value between -1 and 1.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of constructing a 3D array where entries are between -1 and 1. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional components that were not present in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 7 and 20 recite wherein the updating comprises updating at least one of the text encoder and the speech encoder such that a first pair of a speech vector and a training vector in the similarity matrix that has a higher degree of similarity than a second pair of a speech vector and a training vector has a higher similarity score.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of making the design decision to use higher similarity scores as training data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional components that were not present in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 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 –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(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 1-3, 7, 14-16, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Patent Publication US 11798542 B1 (Ryabov et al.).
Regarding Claims 1 and 14, Ryabov et al. teaches A method performed by at least one processor, the method comprising:
(An assistive voice control application may utilize the systems, processes, methods, and techniques described herein to improve voice understanding.) (Col. 45, Lines 34-36).
Claim 14 lists the alternative An apparatus comprising: a memory storing one or more instructions; and a processor operatively coupled to the memory and configured to execute the one or more instructions stored in the memory, wherein the one or more instructions, when executed by the processor, cause the apparatus to:
(An assistive voice control application may utilize the systems, processes, methods, and techniques described herein to improve voice understanding.) (Col. 45, Lines 34-36).
(In some examples, system memory 2116 may store and/or load an operating system 2140 for execution by processor 2114.) (Col. 53, Lines 33-35).
receiving one or more training text sentences;
(At step 1702, a sentence may be taken from a dataset. For example, sentence samples as discussed herein may be taken from a DLM database (e.g., the DLM database 1022 as shown in FIG. 10).) (Col. 42, Lines 17-20).
A sentence is received from a dataset.
generating one or more training vectors based on inputting the one or more training sentences input into a text encoder, the one or more training vectors corresponding to one or more operations that an electronic device is configured to perform;
(Each intent pattern may reflect one or more variations corresponding to how a user of the third-party application may be expected to verbalize the intent corresponding to the intent pattern. For example, FIG. 3 illustrates an intent pattern 302 expressed as “(Play|Listen To) &song (“|track)’, indicating multiple variations corresponding to possible user utterances that may match with the intent of the pattern. The intent pattern 302 may include at least one variable (e.g., &song) indicating a class of entities that may be the subject of the intent pattern (e.g., $song entities).) (Col. 17, Lines 49-62).
(At step 1712, a pattern may be provided to the pattern encoder to produce a pattern vector. For example, the system 1400 may receive the pattern for encoding into a pattern vector. … At step 1716, the sentence with labels may be used to compute any decoding losses. For example, the time distributed dense layer 1510 of the system 1500 may produce labels for each token.) (Col. 42, Lines 33-43).
Sentences corresponding to device operations such as playing a song are turned
generating one or more speech vectors based on one or more speech utterances input into a speech encoder;
(The voice encoder 908 may encode the spoken words of the user into one or more voice packets.) (Col. 28, Lines 26-28).
Input utterances are encoded into vectors.
generating a similarity matrix that compares each of the one or more training vectors with each of the one or more speech vectors;
(In some embodiments, two vectors corresponding to an intent pattern and a user utterance may be compared by calculating a distance between the vectors, wherein a smaller distance indicates that the pattern and the utterance may be more semantically similar in comparison to a larger distance. … The clustering module 204 may be configured to receive the vectors corresponding to each of the patterns of the DLM 116 and may be configured to cluster the received vectors into a plurality of clusters based upon a level of similarity between the vectors. For example, the vectors may be clustered such that vectors having at least a threshold amount of similarity to each other are placed within the same cluster. Similarity between vectors may be measured based upon the distance between the vectors. By clustering the vectors corresponding to the patterns of the DLM 116, received vectors corresponding to user utterances may be more easily matched with the vectors of the DLM 116. ) (Col. 13, Line 65 – Col. 14, Line 18).
The clusters of patterns act as a similarity matrix as the vector distance between the speech utterance and training examples are stored in it.
updating at least one of the text encoder and the speech encoder based on the similarity matrix.
(Decoding losses may be determined (computed) by comparing the output of the time distributed dense layer 1510 to the to the class labels assigned to the tokens of the original sentence according to the noun phrases. At step 1718, gradients and/or other variables for the pattern encoder and the pattern decoder may be updated based on any computed decoding losses.) (Col. 42, Lines 43-49).
Regarding Claims 2 and 15, Ryabov et al. teaches the method of claims 1 and 14.
Ryabov et al. further teaches wherein the one or more training text sentences are received from a supervised dataset that labels each text sentence from the one or more text sentences with a label corresponding to an operation from the one or more operations..
(A script may include a language pattern that includes an entity parameter definition. For example, “Entity: ‘(what is/show/open) the $(T weather/temperature/forecast) for $(DATE) in $(LOC).” This language pattern that includes an entity parameter may be matched to the text “show what the weather will be on Monday in Sunnyvale”. This language pattern may be used to define a DLM: “T.fwdarw.weather, DATE.fwdarw.Monday, LOC.fwdarw.Sunnyvale”.) (Col. 5, Lines 58-64).
(As used herein, the term “dialog context” may refer to one or more groups of declared intents and follows. Intents and follows may define phrase patterns and business logic that may be launched when a user says a matching phrase. For example, intents may be activated at any time, whereas follows may match only in active contexts.) (Col. 5, Lines 18-32).
(To generate a language model, referring to FIG. 10, DLM and script developers (e.g., the DLM and script developer 1028) may provide a training set of sentence samples with marked up entities for each intent to the tutor server 1020 for use by the language model builder server cluster 1016 for generating a language model for storage in the domain language model database 1022.) (Col. 34, Lines 33-49).
The training sentences correspond to declared intents (operations) where the entities correspond to commands and alternative words that are associated with those commands.
Regarding Claims 3 and 16, Ryabov et al. teaches the method of claims 1 and 14.
Ryabov et al. further teaches wherein the similarity matrix comprises comparing each training vector from the one or more training vectors with each speech vector from the one or more speech vectors by determining a similarity score between a respective training vector and a respective speech vector that indicates a degree of similarity between the respective training vector and the respective speech vector.
(In some embodiments, in order to match a transcribed user utterance to an intent of the DLM 116, the domain 114 may use a context embedding model 118 to compare the transcribed user utterance with a plurality of intent patterns of the DLM 116. The context embedding model 118 may be configured to receive textual data, and to convert the textual data into a semantic representation (e.g., a semantic vector) usable for determining a level of semantic similarity between different pieces of text. By comparing the transcribed user utterances to the intent patterns of the DLM 116 based upon semantic similarity instead of direct textual similarity, the transcribed user utterance may be more accurately and efficiently matched to an intent pattern, even if the intent patterns of the DLM 116 may not cover all semantic variations of a given intent.) (Col. 10, Lines 28-42).
(Each intent pattern may reflect one or more variations corresponding to how a user of the third-party application may be expected to verbalize the intent corresponding to the intent pattern. For example, FIG. 3 illustrates an intent pattern 302 expressed as “(Play|Listen To) &song (“|track)’, indicating multiple variations corresponding to possible user utterances that may match with the intent of the pattern. The intent pattern 302 may include at least one variable (e.g., &song) indicating a class of entities that may be the subject of the intent pattern (e.g., $song entities).) (Col. 17, Lines 49-62).
(At step 1712, a pattern may be provided to the pattern encoder to produce a pattern vector. For example, the system 1400 may receive the pattern for encoding into a pattern vector. … At step 1716, the sentence with labels may be used to compute any decoding losses. For example, the time distributed dense layer 1510 of the system 1500 may produce labels for each token.) (Col. 42, Lines 33-43).
The utterance vector is compared to the training vectors stored in the context embedding model which acts as the similarity matrix. Here it founds which intent shows the highest degree of similarity to the user input.
Regarding Claims 7 and 20, Ryabov et al. teaches the method of claims 1 and 14.
Ryabov et al. further teaches wherein the updating comprises updating at least one of the text encoder and the speech encoder such that a first pair of a speech vector and a training vector in the similarity matrix that has a higher degree of similarity than a second pair of a speech vector and a training vector has a higher similarity score.
(The context embedding model 118 may be configured to receive textual data, and to convert the textual data into a semantic representation (e.g., a semantic vector) usable for determining a level of semantic similarity between different pieces of text. By comparing the transcribed user utterances to the intent patterns of the DLM 116 based upon semantic similarity instead of direct textual similarity, the transcribed user utterance may be more accurately and efficiently matched to an intent pattern, even if the intent patterns of the DLM 116 may not cover all semantic variations of a given intent.) (Col. 10, Lines 32-42).
(A sentence encoder may be trained in a supervised manner on a large dataset. A probability of a sentence belonging to the language model, and therefore matching a pattern, may be defined by a proximity of the sentence embedding vector to one or more vectors describing the pattern semantic and by a distance from other patterns.) (Col. 37, Line 64 – Col. 38, Line2).
(At step 1716, the sentence with labels may be used to compute any decoding losses. For example, the time distributed dense layer 1510 of the system 1500 may produce labels for each token. Decoding losses may be determined (computed) by comparing the output of the time distributed dense layer 1510 to the to the class labels assigned to the tokens of the original sentence according to the noun phrases. At step 1718, gradients and/or other variables for the pattern encoder and the pattern decoder may be updated based on any computed decoding losses.) (Col. 42, Lines 40-49).
Encoders are updated based on losses found when comparing sentences to labels.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 11798542 B1 (Ryabov et al.) in view of US 20170294192 A1 (Bradley et al.).
Regarding Claims 4 and 17, Ryabov et al. teaches the system of claims 1 and 14.
Ryabov et al. does not explicitly teach: wherein a sum of each of the similarity scores in each column of the similarity matrix is 1.
However, Bradley et al. teaches wherein a sum of each of the similarity scores in each column of the similarity matrix is 1.
(In some implementations, a conditional PMF may be computed as a matrix where each element of the matrix indicates a conditional probability of an input segment given a reference segment. The conditional PMF matrix may be computed by normalizing each column of the matrix of similarity scores such that each column sums to 1. For example, a conditional PMF may be computed as) (Paragraph 77).
Bradley et al. teaches a similarity matrix in which the columns each sum to 1.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the similarity measuring and training process as taught by Ryabov et al. to include a similarity matrix where the columns sum to 1 as taught by Bradley et al. This would have been an obvious substitution as it does not change the operation of the similarity matrix and is still used for the same intended purpose. Such a substitution would create a predictable result of identifying similarity values and thus there is motivation to combine under KSR.
Claims 5, 6, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 11798542 B1 (Ryabov et al.) in view of US 20100268737 A1 (Podilchuk).
Regarding Claims 5 and 18, Ryabov et al. teaches the system of claims 1 and 14.
Ryabov et al. does not explicitly teach: wherein each diagonal entry in the similarity matrix has a higher similarity score than a non-diagonal entry.
However, Podilchuk teaches wherein each diagonal entry in the similarity matrix has a higher similarity score than a non-diagonal entry.
(Diagonal matrix elements (i.e., matrix elements S,.sub.ij where i=j) relate to similarity scores of the respective reference images versus themselves and, as such, are unity matrix elements (i.e., matrix elements which numerical value is equal to 1). Correspondingly, since similarity scores S.sub.ij and S.sub.ji for the respective reference image 220.sub.ij and 220.sub.ji are equal to one another, the similarity matrix 300 is a symmetric matrix. Numerical value of the non-diagonal matrix elements is disposed in a range from 0 to 1) (Paragraph 29).
Podilchuk teaches a similarity matrix in which diagonals elements are equal to 1 and non-diagonal entries are a smaller value between 0 and 1.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the similarity measuring and training process as taught by Ryabov et al. to include a similarity matrix where diagonal entries are higher values than non-diagonal entries as taught by Podilchuk. This would have been an obvious substitution as it does not change the operation of the similarity matrix and is still used for the same intended purpose. Such a substitution would create a predictable result of identifying similarity values and thus there is motivation to combine under KSR.
Regarding Claims 6 and 19, Ryabov et al. in view of Podilchuk teaches the system of claims 5 and 18.
Furthermore, Podilchuk teaches wherein at least one non-diagonal entry in the similarity matrix has a value between -1 and 1.
(Diagonal matrix elements (i.e., matrix elements S,.sub.ij where i=j) relate to similarity scores of the respective reference images versus themselves and, as such, are unity matrix elements (i.e., matrix elements which numerical value is equal to 1). Correspondingly, since similarity scores S.sub.ij and S.sub.ji for the respective reference image 220.sub.ij and 220.sub.ji are equal to one another, the similarity matrix 300 is a symmetric matrix. Numerical value of the non-diagonal matrix elements is disposed in a range from 0 to 1) (Paragraph 29).
Non-diagonal entries range from 0 to 1 which falls within the range of -1 and 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS DANIEL LOWEN whose telephone number is (571)272-5828. The examiner can normally be reached Mon-Fri 8:00am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D Shah can be reached at (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
09/25/2026
