DETAILED ACTION
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 .
Response to Amendment
The amendment filed 04/15/2026 has been entered. Applicant has amended claims 1-20. No claims have been added or cancelled. Claims 1-20 are currently pending in the instant application.
Response to Arguments
Applicant’s arguments, see pages 6-9, filed 04/15/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in further view of Morse et al (US20230315405). Morse teaches the amended limitations as seen in the current rejection below.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al (US 2025/0209194) in view of Morse et al (US20230315405).
Regarding claim 1, Crabtree teaches
1. A computer-implemented method comprising: receiving a prompt to generate code using a generative artificial intelligence (GenAI) model ([0054] FIG. 1 is a block diagram illustrating an exemplary system architecture 100 for collaborative generative artificial intelligence (AI) content identification and verification, according to an embodiment. According to the embodiment, the system comprises one or more generating services 110 which can employ various generative artificial intelligence systems and/or models to generate a plurality of content in whole or in part including, but not limited to, images, illustration, photo, multimedia, podcast, music, endorsement, artwork, video, audio, text, and computer programming instructions (i.e., software code). A generating service 110 may receive a prompt to generate content from a user and present the generated content 111 to the user or to a downstream application or process.); generating the code using the GenAI model ([0079] FIG. 9 is a block diagram illustrating an exemplary system architecture for collaborative generative artificial intelligence (AI) content identification and verification, according to another embodiment. According to the embodiment, the system comprises one or more generating services 910 which can employ various generative artificial intelligence systems and/or models to generate a plurality of content including, but not limited to, images, illustration, photo, multimedia, podcast, music, endorsement, artwork, video, audio, text, and computer programming instructions (i.e., software code). ); generating a fingerprint for the code generated using the GenAI model ( [0062] - and GenAI content scoring and verification exchange information tied in. In an implementation, an algorithm similar to PageRank or the temporally enhanced PageRank may be implemented against the resulting clusters as determined by the fingerprinting on individual assets as well as their semantic vector space interpretations and/or the relationship graph and/or a knowledge graph of indexed content. ); comparing the fingerprint for the code to one or more fingerprints for code of one or more datasets to determine a match, ([0111] FIG. 13 is a block diagram illustrating an exemplary aspect of an embodiment of a component for GenAI CVX, a similarity subsystem 1300. According to the embodiment, similarity subsystem is present and configured to implement various similarity algorithms 1320 to determine one or more similarity metrics based on a comparison between a received fingerprint 1301, comprising one or more hash values, and registered content 1302 obtained from content database. The one or more similarity metrics may be used to determine a similarity score 1303 which can be used as a measure that represents the likelihood that the content associated with the fingerprint is similar or otherwise matches registered content stored in content database. A score generator 1330 is present and configured to utilize similarity scores of matched content to determine if input content has been generated by AI models in part, or whole.; retrieving metadata associated with the match, wherein the metadata at least includes an indication of any license associated with the matching code ([0147] -Each part is linked to a content group 615, and a single content group may comprise a collection of hash values (i.e., part identifiers). Additionally, each registered part is linked to the generating service 625 which generated the original content submitted to GenAI CVX. In this way, each part may be individually searched, a content group may be searched, and a generating service may be searched at varying levels of granularity. In some embodiments, additional information such as metadata, or information related to the prompt associated with the submitted, generated content may be stored in library 600.); and determining and performing one or more actions in response to the retrieved metadata. ([0146] - The content produced by the one or more generating services may be sent to a GenAI CVX which can register the received content by assigning a content group to a received content, dividing the received content into a plurality of parts, assigning a hash value to each part, and storing the content group, the hash values, and the generating service in a content registration library 600)
Crabtree does not explicitly teach generated using the GenAI model to one or more fingerprints for code of one or more datasets to determine a matching fingerprint from among the fingerprints for code of the one or more dataset; retrieving metadata associated with the matching fingerprint, wherein the metadata at least includes an indication of any license associated with code corresponding to the matching fingerprint; and determining and performing one or more actions in response to the metadata, wherein the one or more actions include blocking the code generated using the GenAI model from being incorporated into a project or providing a notification of the license.
Morse teaches generated using the GenAI model to one or more fingerprints for code of one or more datasets to determine a matching fingerprint from among the fingerprints for code of the one or more dataset; retrieving metadata associated with the matching fingerprint, wherein the metadata at least includes an indication of any license associated with code corresponding to the matching fingerprint ([0034] Entry generator 320 may be implemented by code suggestion metadata store management 216 to generate the content of an entry for the code segment at the location corresponding to the hash value for the entry 319. Logic tree generation 322 may tokenize a code segment by recognizing certain words, symbols, or characterizes (e.g., using regular expression searches) or delimiters (e.g., space character). Logic tree generation 322 may then generate logic trees may be generated from a tokenized (e.g., per word/symbol excluding some words or symbols that are not informative) version of a code segment, where nodes corresponding to the tokens are generated and linked to represent the logic of the code segment. Metadata collection 324 may gather (e.g., from a request or repository) the various metadata to store, such as license information, source information (e.g., source repository), style guidelines or other information. This information may be obtained from internal (e.g., to provider network 200) and/or external (e.g., to provider network 200) sources. In some embodiments, some metadata may be prompted via an interface to be added. As indicated at 325 the logic tree and metadata may be stored in the entry identified by the hash values at code suggestion metadata store 215.); and determining and performing one or more actions in response to the metadata, wherein the one or more actions include blocking the code generated using the GenAI model from being incorporated into a project or providing a notification of the license. ([0026] Code development service may implement code suggestion 213 to generate code suggestions based on text input in development environment 211 or 219 (e.g., utilizing a plug-in or other connection which may provide real-time analysis and suggestion of code as the code is entered into the development environment 211 or 219). Code suggestion 213 may use generative models, machine learning models such as Generative Adversarial Networks (GANs), trained to generate code suggestions. Generative models are often trained on a large corpus of data for a specific task. In the case of generating code recommendations, this corpus of (e.g., from code suggestion code repositories 215 or other code repositories used to train the generative model) can be comprised of code repositories or snippets from a variety of sources. Depending on the source or owner, the code may be subject to certain licenses which need to be attributed in any usage or reproduction. Since a generative model can sometimes reproduce verbatim, or close to verbatim, matches to the training data, metadata for attributing the original source may also need to be provided as part of the suggestion. Code suggestion metadata store management 216 may provide the ability to index code used to train code suggestions and to provide metadata for code suggestions from code metadata store 217 that may be provided, as discussed in detail below.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified the teachings of Crabtree to include generated using the GenAI model to one or more fingerprints for code of one or more datasets to determine a matching fingerprint from among the fingerprints for code of the one or more dataset; retrieving metadata associated with the matching fingerprint, wherein the metadata at least includes an indication of any license associated with code corresponding to the matching fingerprint; and determining and performing one or more actions in response to the metadata, wherein the one or more actions include blocking the code generated using the GenAI model from being incorporated into a project or providing a notification of the license as taught by Morse It would be advantageous since it avoid licensing/legal issues with an user utilizing code that is protected as taught by the cited sections of Morse.
Regarding claim 2, Crabtree in view of Morse teaches The computer-implemented method of claim 1, Crabtree further teaches wherein a query for augmented data is provided with the prompt to generate code the GenAI model, wherein the method further comprises: retrieving one or more files in response to the query; and providing the retrieved one or more files with the prompt to the GenAI model. [0147] As shown, content registration library 600 may act as a corpus of generated content associated with a plurality of generating services. In an embodiment, the library may store information associated with each part 605 of a received generated content. Each part may be assigned an identifier such as a hash value, and this part identifier may be stored in the library. Each part is linked to a content group 615, and a single content group may comprise a collection of hash values (i.e., part identifiers). Additionally, each registered part is linked to the generating service 625 which generated the original content submitted to GenAI CVX. In this way, each part may be individually searched, a content group may be searched, and a generating service may be searched at varying levels of granularity. In some embodiments, additional information such as metadata, or information related to the prompt associated with the submitted, generated content may be stored in library 600.
Regarding claim 3, Crabtree in view of Morse teaches The computer-implemented method of claim 1, Crabtree further teaches wherein the metadata includes one or more of a link to a file, a classification, an indication of a license associated with the file, an indication of usage of generated content, an indication of a hash used to generate the fingerprint, or indication of a model used to produce the content. ([0147] As shown, content registration library 600 may act as a corpus of generated content associated with a plurality of generating services. In an embodiment, the library may store information associated with each part 605 of a received generated content. Each part may be assigned an identifier such as a hash value, and this part identifier may be stored in the library. Each part is linked to a content group 615, and a single content group may comprise a collection of hash values (i.e., part identifiers). Additionally, each registered part is linked to the generating service 625 which generated the original content submitted to GenAI CVX. In this way, each part may be individually searched, a content group may be searched, and a generating service may be searched at varying levels of granularity. In some embodiments, additional information such as metadata, or information related to the prompt associated with the submitted, generated content may be stored in library 600.)
Regarding claim 4, Crabtree teaches A computer-implemented method comprising: receiving a prompt to generate content using a generative artificial intelligence (GenAI) model ([0054] FIG. 1 is a block diagram illustrating an exemplary system architecture 100 for collaborative generative artificial intelligence (AI) content identification and verification, according to an embodiment. According to the embodiment, the system comprises one or more generating services 110 which can employ various generative artificial intelligence systems and/or models to generate a plurality of content in whole or in part including, but not limited to, images, illustration, photo, multimedia, podcast, music, endorsement, artwork, video, audio, text, and computer programming instructions (i.e., software code). A generating service 110 may receive a prompt to generate content from a user and present the generated content 111 to the user or to a downstream application or process.); generating the content using the GenAI model([0079] FIG. 9 is a block diagram illustrating an exemplary system architecture for collaborative generative artificial intelligence (AI) content identification and verification, according to another embodiment. According to the embodiment, the system comprises one or more generating services 910 which can employ various generative artificial intelligence systems and/or models to generate a plurality of content including, but not limited to, images, illustration, photo, multimedia, podcast, music, endorsement, artwork, video, audio, text, and computer programming instructions (i.e., software code). ); generating a fingerprint for the content; comparing the fingerprint for the content to one or more fingerprints for content of one or more datasets to determine a match ([0111] FIG. 13 is a block diagram illustrating an exemplary aspect of an embodiment of a component for GenAI CVX, a similarity subsystem 1300. According to the embodiment, similarity subsystem is present and configured to implement various similarity algorithms 1320 to determine one or more similarity metrics based on a comparison between a received fingerprint 1301, comprising one or more hash values, and registered content 1302 obtained from content database. The one or more similarity metrics may be used to determine a similarity score 1303 which can be used as a measure that represents the likelihood that the content associated with the fingerprint is similar or otherwise matches registered content stored in content database. A score generator 1330 is present and configured to utilize similarity scores of matched content to determine if input content has been generated by AI models in part, or whole; retrieving metadata associated with the match([0147] -Each part is linked to a content group 615, and a single content group may comprise a collection of hash values (i.e., part identifiers). Additionally, each registered part is linked to the generating service 625 which generated the original content submitted to GenAI CVX. In this way, each part may be individually searched, a content group may be searched, and a generating service may be searched at varying levels of granularity. In some embodiments, additional information such as metadata, or information related to the prompt associated with the submitted, generated content may be stored in library 600.); and determining and performing one or more actions in response to the retrieved metadata. ([0146] - The content produced by the one or more generating services may be sent to a GenAI CVX which can register the received content by assigning a content group to a received content, dividing the received content into a plurality of parts, assigning a hash value to each part, and storing the content group, the hash values, and the generating service in a content registration library 600)
Crabtree does not explicitly teach generated using the GenAI model to one or more fingerprints for code of one or more datasets to determine a matching fingerprint from among the fingerprints for code of the one or more dataset; retrieving metadata associated with the matching fingerprint, wherein the metadata at least includes an indication of any license associated with code corresponding to the matching fingerprint; and determining and performing one or more actions in response to the metadata, wherein the one or more actions include blocking the code generated using the GenAI model from being incorporated into a project or providing a notification of the license.
Morse teaches generated using the GenAI model to one or more fingerprints for code of one or more datasets to determine a matching fingerprint from among the fingerprints for code of the one or more dataset; retrieving metadata associated with the matching fingerprint, wherein the metadata at least includes an indication of any license associated with code corresponding to the matching fingerprint ([0034] Entry generator 320 may be implemented by code suggestion metadata store management 216 to generate the content of an entry for the code segment at the location corresponding to the hash value for the entry 319. Logic tree generation 322 may tokenize a code segment by recognizing certain words, symbols, or characterizes (e.g., using regular expression searches) or delimiters (e.g., space character). Logic tree generation 322 may then generate logic trees may be generated from a tokenized (e.g., per word/symbol excluding some words or symbols that are not informative) version of a code segment, where nodes corresponding to the tokens are generated and linked to represent the logic of the code segment. Metadata collection 324 may gather (e.g., from a request or repository) the various metadata to store, such as license information, source information (e.g., source repository), style guidelines or other information. This information may be obtained from internal (e.g., to provider network 200) and/or external (e.g., to provider network 200) sources. In some embodiments, some metadata may be prompted via an interface to be added. As indicated at 325 the logic tree and metadata may be stored in the entry identified by the hash values at code suggestion metadata store 215.); and determining and performing one or more actions in response to the metadata, wherein the one or more actions include blocking the code generated using the GenAI model from being incorporated into a project or providing a notification of the license. ([0026] Code development service may implement code suggestion 213 to generate code suggestions based on text input in development environment 211 or 219 (e.g., utilizing a plug-in or other connection which may provide real-time analysis and suggestion of code as the code is entered into the development environment 211 or 219). Code suggestion 213 may use generative models, machine learning models such as Generative Adversarial Networks (GANs), trained to generate code suggestions. Generative models are often trained on a large corpus of data for a specific task. In the case of generating code recommendations, this corpus of (e.g., from code suggestion code repositories 215 or other code repositories used to train the generative model) can be comprised of code repositories or snippets from a variety of sources. Depending on the source or owner, the code may be subject to certain licenses which need to be attributed in any usage or reproduction. Since a generative model can sometimes reproduce verbatim, or close to verbatim, matches to the training data, metadata for attributing the original source may also need to be provided as part of the suggestion. Code suggestion metadata store management 216 may provide the ability to index code used to train code suggestions and to provide metadata for code suggestions from code metadata store 217 that may be provided, as discussed in detail below.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified the teachings of Crabtree to include generated using the GenAI model to one or more fingerprints for code of one or more datasets to determine a matching fingerprint from among the fingerprints for code of the one or more dataset; retrieving metadata associated with the matching fingerprint, wherein the metadata at least includes an indication of any license associated with code corresponding to the matching fingerprint; and determining and performing one or more actions in response to the metadata, wherein the one or more actions include blocking the code generated using the GenAI model from being incorporated into a project or providing a notification of the license as taught by Morse It would be advantageous since it avoid licensing/legal issues with an user utilizing code that is protected as taught by the cited sections of Morse.
Regarding claim 5, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches wherein the content is code. [0079] FIG. 9 is a block diagram illustrating an exemplary system architecture for collaborative generative artificial intelligence (AI) content identification and verification, according to another embodiment. According to the embodiment, the system comprises one or more generating services 910 which can employ various generative artificial intelligence systems and/or models to generate a plurality of content including, but not limited to, images, illustration, photo, multimedia, podcast, music, endorsement, artwork, video, audio, text, and computer programming instructions (i.e., software code)
Regarding claim 6, Crabtree in view of Morse teaches The computer-implemented method of claim 5, Crabtree further teaches wherein the one or more datasets includes of one or more code repositories. ([0054] FIG. 1 is a block diagram illustrating an exemplary system architecture 100 for collaborative generative artificial intelligence (AI) content identification and verification, according to an embodiment. According to the embodiment, the system comprises one or more generating services 110 which can employ various generative artificial intelligence systems and/or models to generate a plurality of content in whole or in part including, but not limited to, images, illustration, photo, multimedia, podcast, music, endorsement, artwork, video, audio, text, and computer programming instructions (i.e., software code). A generating service 110 may receive a prompt to generate content from a user and present the generated content 111 to the user or to a downstream application or process. Examples of generation services which may be integrated with the generative AI content verification exchange 200 include OpenAI's GPT or DALLE, Midjourney, Grok, BERT, Gemini, StyleGAN, DALL-E, Mistral, WaveGAN, Deepfake, Amazon Bedrock and/or the like.)
Regarding claim 7, Crabtree in view of Morse teaches The computer-implemented method of claim 6, Crabtree further teaches wherein the of one or more code repositories are subject to one or more open source licenses. ([0146] FIG. 6 is a diagram illustrating an exemplary content registration library 600, according to an embodiment. According to the embodiment, the content registration library 600 may be configured to store information associated with registered content. Note that the system may also bulk upload or incorporate licensed data sets or crawled data sets to aid in seeding the overall corpus of data available for analysis. I
Regarding claim 8, Crabtree in view of Morse teaches The computer-implemented method of claim 5, Crabtree further teaches wherein the one or more actions are depending on an open source license of the one or more open source licenses. ([0146] FIG. 6 is a diagram illustrating an exemplary content registration library 600, according to an embodiment. According to the embodiment, the content registration library 600 may be configured to store information associated with registered content. Note that the system may also bulk upload or incorporate licensed data sets or crawled data sets to aid in seeding the overall corpus of data available for analysis. I
Regarding claim 9, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches wherein a query for augmented data is provided with the prompt to generate content using a generative artificial intelligence (GenAI) model, wherein the method further comprises: retrieving one or more files in response to the query; and providing the retrieved one or more files with the prompt to the GenAI model. ([0148] FIG. 7 is a flow diagram illustrating an exemplary method 700 for registering received content using a GenAI CVX, according to an embodiment. According to the embodiment, the process begins at step 701 when GenAI CVX 200 receives generated content from a generating service 110. In an embodiment, the received content is multimedia content, wherein the multimedia content may be generated by a generating service. At step 702, the submitted content is assigned a content group. This content group may be denoted by an unique identifier. The content group may act as a global identifier for any constituent parts or derivative content derived from the submitted content.)
Regarding claim 10, Crabtree in view of Morse teaches The computer-implemented method of claim 9, Crabtree further teaches further comprising: generating a fingerprint for the retrieved one or more files; associating metadata with the generated a fingerprint for the retrieved one or more files; and storing the generated fingerprint for the retrieved one or more files and the associated metadata. ([0147] As shown, content registration library 600 may act as a corpus of generated content associated with a plurality of generating services. In an embodiment, the library may store information associated with each part 605 of a received generated content. Each part may be assigned an identifier such as a hash value, and this part identifier may be stored in the library. Each part is linked to a content group 615, and a single content group may comprise a collection of hash values (i.e., part identifiers). Additionally, each registered part is linked to the generating service 625 which generated the original content submitted to GenAI CVX. In this way, each part may be individually searched, a content group may be searched, and a generating service may be searched at varying levels of granularity. In some embodiments, additional information such as metadata, or information related to the prompt associated with the submitted, generated content may be stored in library 600
Regarding claim 11, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches wherein the metadata includes one or more of a link to a file, a classification, an indication of a license associated with the file, an indication of usage of generated content, an indication of a hash used to generate the fingerprint, and an indication of a model used to produce the content. [0085] GenAI CVX 900 can provide content similarity and/or verification capabilities. When an external party wants to check if content is possibly generated by AI or a content creator 920 or otherwise is registered content stored in a content database, they can submit the content 120 to GenAI CVX 900 for verification. According to an embodiment, during content similarity and/or verification GenAI CVX 900 may break up the input content 120 (e.g., image or text) into random and/or pseudorandom parts (e.g., sub-images and phrases respectively), call candidate groups and compare hashes across the known corpus of submitted content at the global datastore level. If there is a match for any of the hashes,
Regarding claim 12, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches further comprising: building a collection of fingerprints and associated metadata for one or more datasets. ([0085] GenAI CVX 900 can provide content similarity and/or verification capabilities. When an external party wants to check if content is possibly generated by AI or a content creator 920 or otherwise is registered content stored in a content database, they can submit the content 120 to GenAI CVX 900 for verification. According to an embodiment, during content similarity and/or verification GenAI CVX 900 may break up the input content 120 (e.g., image or text) into random and/or pseudorandom parts (e.g., sub-images and phrases respectively), call candidate groups and compare hashes across the known corpus of submitted content at the global datastore level. If there is a match for any of the hashes,
Regarding claim 13, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches, wherein generating a fingerprint for the content comprises: word tokenizing the content; performing a first hashing of the tokenized words to generate a set of values; and minimizing the generated set of values. ([0090] According to the embodiment, data chunker 1020 is configured to deconstruct content into a plurality of data chunks. The size of the deconstructed data chunks may be predefined, for example each data chunk may be 64 bits. The size of the data chunks may be based on the type of content being deconstructed, wherein the input content is identified prior to being deconstructed, and the result of the analyses and is used to determine the size of the data chunks. Text data can be segmented into smaller parts using techniques such as tokenization or natural language processing methods. Tokenization breaks the text into individual words or phrases,)
Regarding claim 14, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches wherein comparing the fingerprint for the content to one or more fingerprints for content of one or more datasets to determine a match, wherein the one or more datasets have been used to train the GenAI model comprises accessing one or more storage locations that store fingerprints according to a locality sensitive hash. ([0081] A collection of hash values which represent a generated content 911 from a generation service 910 may be referred to as a “content group.” When generating service 910 generates new content 911, GenAI CVX 900 simultaneously creates content group hash data associated with the received generated content. Each submission to GenAI CVX 900 can be stored as a related content group and each hash may be independently searchable at a global datastore level.)
Regarding claim 15, Crabtree in view of Morse teaches The computer-implemented method of claim 4, Crabtree further teaches wherein one or more datasets have been used to influence the GenAI model. ([0071] According to an aspect of an embodiment, system 200 can leverage federated learning techniques that allow multiple parties (e.g., content creators, hosting platforms, regulatory bodies) to collaboratively train and refine content classification and similarity detection models without sharing raw data across jurisdictional boundaries. This may involve developing interoperability standards and protocols that enable seamless integration and data exchange between different national or regional content registration systems, while respecting local laws and regulations. In an implementation, the system may utilize smart contract-based governance mechanisms that allow stakeholders to define and enforce content registration policies and dispute resolution procedures in a transparent and auditable manner, even in the face of conflicting national interests or political constraints.)
Claims 16-20 are rejected using similar reasoning seen in the rejection of claims 4-6 and 13 due to reciting similar limitations but directed towards a system.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL SHARPLESS whose telephone number is (571)272-1521. The examiner can normally be reached M-F 7:30 AM- 3:30 PM (ET).
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/S.C.S./Examiner, Art Unit 2165
/ALEKSANDR KERZHNER/Supervisory Patent Examiner, Art Unit 2165
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