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 .
The Amendment filed on 02/19/26 has been received and entered. Application No. 18/815,083 of which claims 1-20 have been canceled and claim 21-40 are added. Claims 21-40 are pending in the application, all of which are ready for examination by the examiner.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/19/2026 has been entered.
Response to Amendment
Applicant’s amendment necessitated new grounds of rejection with regards to cancellation of the claims.
Applicant’s response, filed on 02/19/26, with respect to 101 rejections directed to an abstract idea of claims 1-20 have been withdrawn with regards to cancellation of the claims. The rejections are withdrawn.
Response to Arguments
Applicant's arguments with respect to 35 USC § 101 rejections of claims 1-20 have been withdrawn with regards to cancellation of the claims.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 21, 28 and 35 recite determining, by a context classification engine executed on the processor, at least one context from a predetermined list of contexts based on the extracted visual features and audio features; comparing, at the remote system, the transmitted embedding vector with a plurality of reference embedding vectors stored in a database to identify a content entry associated with a reference embedding vector having a similarity to the transmitted embedding vector above a predetermined threshold.
The limitations of determining…, comparing…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “determining…, comparing…,” in the context of these claims encompass the user manually determining context from a predetermined list of contexts, comparing transmitted embedding vector with reference embedding vectors. 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 claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – causing…, preprocessing…, extracting…, retrieving…, generating…, applying…, storing.., transmitting…. The “causing”, “preprocessing”, “generating” and “applying” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). The “extracting”, “retrieving”, “storing”, and “transmitting” limitations are insignificant extra-solution activity (mere data gathering, data output please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Causing”, “preprocessing”, “generating” and “applying” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements of “extracting”, “retrieving”, “storing”, and “transmitting” is a well-understood, routine, and conventional activity (data gathering, data output see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 22, 29, and 36 recites wherein: generating the embedding vector comprises: generating, by the on-device neural inference engine, a respective intermediate embedding for each of the one or more tokens corresponding to the extracted visual features, the audio features, and the additional contextual data; and aggregating the intermediate embeddings into an embedding array; and the dimensionality reduction is applied to the embedding array. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., generating, aggregating) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 23, 30, and 37 recites wherein comparing the transmitted embedding vector with the plurality of reference embedding vectors comprises performing a cosine similarity operation. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., comparing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 24, 31, and 38 recites wherein identifying the content entry comprises: applying, at the remote system, a decoder portion of the trained encoder-decoder neural network to the transmitted embedding vector to generate a candidate content representation; and comparing the candidate content representation with stored content representations associated with the plurality of reference embedding vectors. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., comparing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 25, 32, and 39 recites generating, at the remote system, a contextual description of the real-world environment based on the transmitted embedding vector; providing the contextual description to one or more content providers; receiving one or more content entries from the one or more content providers; and applying the encoder portion of the trained encoder-decoder neural network to the one or more content entries to generate corresponding reference embedding vectors stored in the database. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., generating, providing, receiving, applying) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 26 and 33 recites wherein generating the unified textual representation comprises representing each of the extracted visual features, the audio features, and the additional contextual data as a respective token of the one or more tokens. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., generating) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 27, 34 and 40 recites identifying, by the processor, a secondary device located within a predetermined operational proximity to the user device; and causing the secondary device to output the information associated with the identified content entry. The limitations only recite additional elements at a high level of generality. These limitations are recited at a high-level of generality (i.e., identifying, causing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
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.
Claims 21-40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA), 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 pre-AlA the applicant regards as the invention.
Claims 21-40 recite the limitation “less identifiable”. The term "less identifiable" in claims 21, 28 and 35 is a relative term which renders the claim indefinite. The term "less identifiable" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner will not give any patentable weight to these limitations and will interpret the remaining claim limitations, as ‘wherein the embedding vector is the captured multi-modal data”.
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 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 of this title, 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 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Francis et al. (U.S. PGPub 2025/0022296; hereinafter “Francis”) in view of Tran (U.S. PGPub 2022/0237368) and further in view of Osuala et at. (U.S. PGPub 2022/0261428; hereinafter “Osuala”).
As per claims 21, 28 and 35, Francis discloses a computer-implemented method for semantically interpreting a real-world environment, comprising: causing, by a sensor control module executed by a processor of a user device, a plurality of onboard sensors including at least an image sensor and an audio sensor to capture multi-modal data representing the real-world environment of the user device; (See Fig. 4C, paras. 9, 57-58, 60, wherein real world events, audio-semantic feature embeddings, semantic audio-visual are disclosed; as taught by Francis.)
preprocessing, by a local signal processing module stored in a non-transitory memory of the user device, the captured multi-modal data by: (See paras. 28-29, 32, 62, wherein multimodal dataset are disclosed, also See Figs. 5-11, paras. 72, 81-82, wherein sensor signals are disclosed; as taught by Francis.)
extracting visual features from image frames using image-processing architecture configured to identify features in data expressed as an array, and extracting audio features from audio signals using an audio analysis process; (See paras. 58-62, 70, wherein extracting features process are disclosed, also See Figs. 3A-3B, paras. 54-55, 57, wherein audio capturing array, image capturing device features are disclosed; as taught by Francis.)
determining, by a context classification engine executed on the processor, at least one context from a predetermined list of contexts based on the extracted visual features and audio features; (See paras. 33-35, 74, wherein context of audio-visual navigation (AVN) tasks in which “methods of the present disclosure may implement a modular framework for semantic audio-visual navigation that includes pre-trained and knowledge-enhanced scene priors, thereby facilitating improved unseen generalization” [0033] and “system 400 implements a framework for leveraging knowledge-enhanced scene priors, in the form of object and region semantics, for the semantic audio-visual navigation task. Performance improvements over strong baselines in multiple unseen contexts are achieved (analogous to determining context from predetermined list of contexts), particularly in conditions where the agent needs to find novel sounding objects. The knowledge graph 432 for training models, a curated visual dataset, and a new task definition are also provided, each configured for developing and assessing model generalization performance in unseen environments” [0074] are disclosed, also See paras. 49, 52-53, wherein predetermined features are disclosed; as taught by Francis.)
selectively retrieving, by a network interface controller of the user device and based on the determined context, additional contextual data from a remote system, wherein the selective retrieving is configured to limit data comparison to content entries associated with the determined context; (See paras. 30-31, wherein implementation process on the use of knowledge-enhanced priors in which “a trained model may generalize to novel sound sources. Further, a modular training paradigm may provide improvements in cross-domain generalizability and more tractable optimization (analogous to additional data). Agent performance can be evaluated against a set of novel sounding objects that were not introduced during training” [0031] are disclosed, also See Fig. 4C, 5-11, paras. 74-81, 106, wherein retrieving information process are disclosed; as taught by Francis.)
However, Francis fails to disclose generating, by a local multimodal fusion module, a unified textual representation by converting the extracted visual features, audio features, and additional contextual data into one or more tokens including natural language text; applying, by an on-device neural inference engine, an encoder portion of a trained encoder-decoder neural network to the unified textual representation to generate an embedding vector; storing, in a memory of the user device, the embedding vector; transmitting, by the network interface controller, the embedding vector to the remote system, wherein the embedding vector is less identifiable with a user than the captured multi-modal data; comparing, at the remote system, the transmitted embedding vector with a plurality of reference embedding vectors stored in a database to identify a content entry associated with a reference embedding vector having a similarity to the transmitted embedding vector above a predetermined threshold; and causing, by the processor, the user device to output information associated with the identified content entry.
On the other hand, Tran teaches generating, by a local multimodal fusion module, a unified textual representation by converting the extracted visual features, audio features, and additional contextual data into one or more tokens including natural language text; (See paras. 269, 277-278, wherein process of converting content are disclosed, also See paras. 188-189, 226, wherein decoder-only transformer and transformer language model features and tokens in which “the transformers described herein and trained to analyze emotion based on the video/sound/text. A transformer model is used to fuse audio-visual-text modalities on the model level. A multi-head attention produces multimodal emotional intermediate representations from common semantic feature space after encoding text, audio and visual modalities, as supplemented by long-term temporal dependencies with self-attention” [0226] are disclosed; as taught by Tran.)
applying, by an on-device neural inference engine, an encoder portion of a trained encoder-decoder neural network to the unified textual representation to generate an embedding vector; (See Fig. 2B, paras. 70-75, 95, 189, wherein generating embedding for words are disclosed; as taught by Tran.)
storing, in a memory of the user device, the embedding vector; (See paras. 96-98, 101-102, wherein clustering generated embeddings into clusters are disclosed, also See paras. 67-68, 303, wherein stored data are disclosed; as taught by Tran.)
transmitting, by the network interface controller, the embedding vector to the remote system, wherein the embedding vector is less identifiable with a user than the captured multi-modal data; (See paras. 303-305, 307, 430, wherein transmitting data to a remote computer are disclosed, also See paras. 67-68, 226, wherein vectors, audio-visual-text modalities are disclosed; as taught by Tran.)
comparing, at the remote system, the transmitted embedding vector with a plurality of reference embedding vectors stored in a database to identify a content entry associated with a reference embedding vector having a similarity to the transmitted embedding vector above a predetermined threshold; (See Fig. 1C, paras. 57, 67-69, 169, wherein determining code similarity in which “Code similarity measurement (such as vector dot product, cosine similarity) is used to determine the similarity score between the input program and any other program that has undergone the same code transformation process” [0067] are disclosed; as taught by Tran.)
and causing, by the processor, the user device to output information associated with the identified content entry. (See paras. 96, 178-178, 181-182, wherein output embedding, encoder outputs are disclosed; as taught by Tran.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Tran teachings in the Francis system. Skilled artisan would have been motivated to incorporate system for machine content generation taught by Tran in the Francis system for effective knowledge-driven scene priors for semantic audio-visual embodied navigation. In addition, both of the references (Francis and Tran) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, temporal dependent retrieval. This close relation between both of the references highly suggests an expectation of success.
However, the combination of Francis and Tran fails to disclose applying a dimensionality reduction to the embedding vector.
On the other hand, Osuala teaches applying a dimensionality reduction to the embedding vector. (See paras. 47, 52, wherein word embedding models features in which “feeding the word embedding model may cause a reduction of dimensionality of the words or word representations that are fed into the model. As the dimension of the output space may be lower than the dimension input space, a mapping of a discrete representation of the words of the sequence to the continuous vector space may be considered as a projection of the discrete representation of the words of the sequence to the output space” [0052] are disclosed, also See para. 67, wherein discarding output embeddings process in which “query word embedding 412b is generated as output of the deep neural network 404. Other output embeddings such as the first and second output embeddings 414a, 414b are discarded from the text searching process 200 as being irrelevant, because the query word embedding 412b will be used for comparison to embeddings of text portions of the other texts… Using the context words linked to the query word that was embedded within the context words results in more meaningful and relevant search results that would occur if the query word and the context words were searched with separate submission to an embedding model. This linking simplifies and reduces computational requirements that are required as compared to a separate submission of the query word and the context words to the embedding model” [0067] are disclosed; as taught by Osuala.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Osuala teachings in the combination of Francis and Tran system. Skilled artisan would have been motivated to incorporate system for selection-based searching using concatenated word and context taught by Osuala in the combination of Francis and Tran system for effective knowledge-driven scene priors for semantic audio-visual embodied navigation. In addition, both of the references (Francis, Tran, and Osuala) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, temporal dependent retrieval. This close relation between both of the references highly suggests an expectation of success.
As per claims 22, 29, and 36, the combination of Francis and Osuala fails to disclose wherein: generating the embedding vector comprises: generating, by the on-device neural inference engine, a respective intermediate embedding for each of the one or more tokens corresponding to the extracted visual features, the audio features, and the additional contextual data; and aggregating the intermediate embeddings into an embedding array; and the dimensionality reduction is applied to the embedding array.
On the other hand, Tran teaches wherein: generating the embedding vector comprises: generating, by the on-device neural inference engine, a respective intermediate embedding for each of the one or more tokens corresponding to the extracted visual features, the audio features, and the additional contextual data; (See paras. 269, 277-278, wherein process of converting content are disclosed, also See paras. 188-189, 226, wherein decoder-only transformer and transformer language model features and tokens in which “the transformers described herein and trained to analyze emotion based on the video/sound/text. A transformer model is used to fuse audio-visual-text modalities on the model level. A multi-head attention produces multimodal emotional intermediate representations from common semantic feature space after encoding text, audio and visual modalities, as supplemented by long-term temporal dependencies with self-attention” [0226] are disclosed; as taught by Tran.)
and aggregating the intermediate embeddings into an embedding array; (See paras. 178-179, 182-183, wherein aggregating output embedding are disclosed; as taught by Tran.)
and the dimensionality reduction is applied to the embedding array. (See Fig. 2B, paras. 68, 70-75, 102 and 352, wherein latent vector, encoder-decoder network training in minimizing sentence reconstruction are disclosed; as taught by Tran.)
See claims 21, 28, and 35 for motivation above.
As per claims 23, 30, and 37, the combination of Francis and Osuala fails to disclose wherein comparing the transmitted embedding vector with the plurality of reference embedding vectors comprises performing a cosine similarity operation.
On the other hand, Tran teaches wherein comparing the transmitted embedding vector with the plurality of reference embedding vectors comprises performing a cosine similarity operation. (See Fig. 1C, paras. 57, 67-69, 169, wherein determining code similarity in which “Code similarity measurement (such as vector dot product, cosine similarity) is used to determine the similarity score between the input program and any other program that has undergone the same code transformation process” [0067] are disclosed, also See Figs. 4D-4E, paras. 95, 330, wherein cosine functions are disclosed; as taught by Tran.)
See claims 21, 28, and 35 for motivation above.
As per claims 24, 31, and 38, the combination of Francis and Osuala fails to disclose wherein identifying the content entry comprises: applying, at the remote system, a decoder portion of the trained encoder-decoder neural network to the transmitted embedding vector to generate a candidate content representation; and comparing the candidate content representation with stored content representations associated with the plurality of reference embedding vectors.
On the other hand, Tran teaches wherein identifying the content entry comprises: applying, at the remote system, a decoder portion of the trained encoder-decoder neural network to the transmitted embedding vector to generate a candidate content representation; (See paras. 72-75, 98, 102, wherein encoder-decoder network training features are disclosed, also See Figs. 4D-4E, paras. 303-305, 330, wherein transmitting data to a remote computer are disclosed, also See paras. 67-68, 226, wherein vectors, audio-visual-text modalities are disclosed, also See Figs. 2A-2I, paras. 260-261, wherein content generation features are disclosed; as taught by Tran.)
and comparing the candidate content representation with stored content representations associated with the plurality of reference embedding vectors. (See Fig. 1C, paras. 57, 67-69, 169, wherein determining code similarity in which “Code similarity measurement (such as vector dot product, cosine similarity) is used to determine the similarity score between the input program and any other program that has undergone the same code transformation process” [0067] are disclosed, also See para. 64, wherein extracting references process are disclosed; as taught by Tran.)
See claims 21, 28, and 35 for motivation above.
As per claims 25, 32, and 39, the combination of Francis and Osuala fails to disclose generating, at the remote system, a contextual description of the real-world environment based on the transmitted embedding vector; providing the contextual description to one or more content providers; receiving one or more content entries from the one or more content providers; and applying the encoder portion of the trained encoder-decoder neural network to the one or more content entries to generate corresponding reference embedding vectors stored in the database.
On the other hand, Tran teaches generating, at the remote system, a contextual description of the real-world environment based on the transmitted embedding vector; (See paras. 227, 306, wherein real time multimodal emotion recognition features are disclosed, also See paras. 303-305, 307, 430, wherein transmitting data to a remote computer are disclosed, also See paras. 67-68, 226, wherein vectors, audio-visual-text modalities are disclosed; as taught by Tran.)
providing the contextual description to one or more content providers; (See Fig. 1B-1D, paras. 53, 58-61, 90-92, wherein descriptions of drawings, detailed description are disclosed; as taught by Tran.)
receiving one or more content entries from the one or more content providers; (See Figs. 1B-1E, paras. 98-99, 257-261, wherein content generation process are disclosed; as taught by Tran.)
and applying the encoder portion of the trained encoder-decoder neural network to the one or more content entries to generate corresponding reference embedding vectors stored in the database. (See Fig. 2B, paras. 68, 102 and 352, wherein latent vector, encoder-decoder network training in minimizing sentence reconstruction are disclosed, also See paras. 96-98, wherein clustering generated embeddings into clusters are disclosed, also See paras. 67, 303, wherein stored data are disclosed, also See Fig. 2B, paras. 70-75, 95, 189, wherein generating embedding for words are disclosed; as taught by Tran.)
See claims 21, 28, and 35 for motivation above.
As per claims 26 and 33, the combination of Francis and Osuala fails to disclose wherein generating the unified textual representation comprises representing each of the extracted visual features, the audio features, and the additional contextual data as a respective token of the one or more tokens.
On the other hand, Tran teaches wherein generating the unified textual representation comprises representing each of the extracted visual features, the audio features, and the additional contextual data as a respective token of the one or more tokens. (See paras. 269, 277-278, wherein process of converting content are disclosed, also See paras. 188-189, 226, wherein decoder-only transformer and transformer language model features and tokens in which “the transformers described herein and trained to analyze emotion based on the video/sound/text. A transformer model is used to fuse audio-visual-text modalities on the model level. A multi-head attention produces multimodal emotional intermediate representations from common semantic feature space after encoding text, audio and visual modalities, as supplemented by long-term temporal dependencies with self-attention” [0226] are disclosed, also See Figs. 2B, paras. 71-75, 94, wherein tokenization process are disclosed; as taught by Tran.)
See claims 21, 28, and 35 for motivation above.
As per claims 27, 34 and 40, the combination of Francis and Osuala fails to disclose identifying, by the processor, a secondary device located within a predetermined operational proximity to the user device; and causing the secondary device to output the information associated with the identified content entry.
On the other hand, Tran further discloses identifying, by the processor, a secondary device located within a predetermined operational proximity to the user device; (See paras. 306, wherein supplementing proximity data in tracking people are disclosed; as taught by Tran.)
and causing the secondary device to output the information associated with the identified content entry. (See paras. 182, 331, 352, wherein encoder outputs in which “generator uses a deep convolutional network that inputs low-dimensional random noise and outputs a video. Spatiotemporal up-convolutions (2D for space, 1D for time) are used to model video. The generator also models the background separately from the foreground. The network produces a static background (which is replicated over time) and a moving foreground that is combined using a mask” [0352] are disclosed; as taught by Tran.)
Conclusion
1. The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
2. When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
POINT OF CONTACT
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIN LIN M HTAY whose telephone number is (571)272-7293. The examiner can normally be reached on M-F, 7am-3pm, PST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L. L. H./
Examiner, Art Unit 2153
/KAVITA STANLEY/ Supervisory Patent Examiner, Art Unit 2153
