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 .
Status of Claims
Claims 1-20 are pending of which claims 1, 10 and 16 are in independent form.
Claims 1-20 are rejected under 35 U.S.C. 101 including (Abstract idea).
Claims 1-20 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the arguments presented for 35 USC 101 rejection, examiner specifies that the arguments/amendments (pages 8-12 of the remarks) fail to overcome the 35 USC 101 rejection. More specifically:
Applicant’s arguments have been considered but are not persuasive. Although applicant alleges that the claimed invention reduces computational and power resource associated with AI processing, the claims do not recite a specific technological improvement to computer functionality, vector indexing technology, semantic retrieval technology, or generative AI model operation. Rather the claims recite receiving a query, generating vectors representations, identifying semantically similar information. selecting candidate document vectors, generating a summary, and presenting results. These limitations are directed to analyzing, comparing, selecting, summarizing, and presenting information, which constitute mental process.
Furthermore, the recited inverted document index, contextualized embeddings, document vectors and generative AI model are claimed at a high level or generality and are used simply as tools to perform the abstract idea. Any suspected improvement in efficiency results from limiting he information provided to the AI model after the abstract process of evaluating and selecting information has been performed, rather than from a specific improvement to computer technology itself. Therefore, the judicial exception is not integrated into a practical application.
The rejection is therefore sustained.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) using GenAI to create a semantic search and summarization for documents.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 are directed to a method, which is a process. Independent claim 10 is directed to a non-transitory computer readable storage medium which falls within one of the 4 statutory categories. Independent claim 13 is directed to an apparatus, comprising: a memory …; and processing circuitry, which is directed to one of the four statutory subject matters. All other claims depend on claims 1, 10 and 16. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, Prong One, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
“receiving a search query for information within an electronic document in a natural language representation” the limitation as drafted recites receiving information for subsequent evaluation and analysis.
“generating, …, a contextualized embedding for the search query to form a search vector” the limitation as drafted recites creating a representation of information for the purpose of comparison and evaluations.
“selecting a set of candidate document vectors … that are semantically similar” the limitation as drafted recites a mental process involving evaluating similarity, and identifying relevant information based on the comparison,
These limitations correspond to concept that can be performed in the human mind (Mental Process) and mathematical algorithms therefore fall within the mental process/mathematical concept category of abstract idea (see MPEP 2016.04(a)(2)).
These steps involve reviewing information, identifying similar content, selecting relevant information, summarizing information, which is a form of mental process, all of which are recognized categories of abstract idea.
Nothing in the claim requires a new QP analysis, new hardware structure, improved storage technique, or any specific technological improvement.
With respect to step 2A, Prong Two, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
"a search model" the limitation as drafted recites genetic software component used as tool to perform the abstract idea.
“split the electronic document into a plurality of information blocks” the limitation as drafted recites conventional information categorization for subsequent analysis. There is no technological improvement.
“each information block … encoded into one or more contextualized embeddings corresponding to one or more document vectors” the limitation as drafted recites conventional information conversion for analysis and comparison. There is no technological improvement.
“the plurality of document vectors forming an inverted document index” the limitation as drafted recites conventional storing and organizing of information for retrieval purposes. There is no technological improvement.
“sending a request to a generative artificial intelligence (Al) model for an abstractive summary of the electronic document content” recites genetic computer function which is insignificant extra-solution activity (performed on a generic/off the shelf computer system) such as receiving the GenAI created summary (i.e. receive information) such as “Mere Data Gathering” and/or “Selecting a particular data source or type of data to be manipulated” and/or “Insignificant Application” as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
“generating an interactive graphical user interface view including a plurality of graphical user interface elements representative of at least one of: the abstractive summary or one or more of the set of candidate document vectors” recites genetic computer function which is insignificant extra-solution activity (performed on a generic/off the shelf computer system) such as using a GUI to display summaries (i.e. GUI) such as “Insignificant Application” as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to a computer readable storage medium, computer, memory, and processor, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition, pages 2-5 of the published instant specification describe generic off‐the‐shelf computer‐based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Regarding claims 2, 11 and 17:
The claims merely recite the source of input (search box/click event). This a a generic user interface action, which the courts consistently treat as data gathering (Abstract).
The claims do not improve GUI technology, browser technology, or network operations.
Regarding claims 3, 12 and 18:
The claims merely identify a particular content type (unsigned agreement) and metadata fields. Narrowing the type of information processed, does not transform an abstract idea into a practical application.
There are no technological improvement to document formats, signature system, or security mechanism.
Regarding claims 4, 13 and 19:
The claims recite a vector embedding, which a mathematical representation of linguistic content. Mathematical models and vector embeddings are mathematical concepts, expressly listed as abstract idea.
The claims do not improve embedding generation.
Regarding claims 5, 14 and 20:
Training BERT encoders using typical transformer layers is a generic machine learning task. This does not change or improve the architecture itself.
Machine learning training, without a technical improvement, is treated as abstract idea (mathematical concept/modeling).
Regarding claims 6 and 15:
The claim merely describes use of a known ML architecture (BERT transformer)> the claims do not recite ant modification to BERT or improved transformer structure.
Reciting a specific model used to carry out and abstract idea does not integrate it into a practical application.
Regarding claim 7:
The claim recites, steps of “generating embeddings”, “indexing embeddings”, “storing in a database”, which all are routine NLP and database operations.
There is no new database indexing mechanism, no improvement to BERT, no Improvement to memory architecture.
Regarding claim 8:
The claim recites granularity choice for embedding. This merely restricts the type of data being processed. The claim does not improve performance, architecture, hardware or computation.
Regarding claim 9:
The claim recites “semantic ranking algorithm” which is considered mathematical computation. Ranking, similarity comparison, and semantic scoring are all abstract idea (information analysis).
There are no improvements to ranking algorithm or retrieval engine.
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 are rejected under 35 U.S.C. 103 as being unpatentable over Matei; Liviu Sebastian et al. (US 11983209 B1) [Matei] in view of Socher; Richard et al. (US 20240020538 A1) [Socher] in view of Bender; Walter et al. (US 20210294829 A1) [Bender] in view of Tran; Bao et al. (US 20230252224 A1) [Tran].
Regarding claims 1, 10 and 16, Matei discloses, a method, comprising: receiving a search query (inputs 106 may include queries upon which the search management system 102 may execute search operations [col. 4, ll. 9-26], The search management system performs the search queries [col. 2, ll. 42-46], receiving a query to search the data repository [col. 9, ll. 59-63]) for information within an electronic document (system selects the document partitions that are to be presented in response to the query [col. 2, ll. 17-32], target feature vector may represent a target partition from a set of document partitions [col. 5, ll. 55-col. 6, ll. 7], the query result may include the document partition corresponding to the target feature vector [col. 10, ll. 49-63]) in a natural language representation (query includes questions [col. 3, ll. 38-55], contextual search [col. 2, ll. 42-46], [col. 2, ll. 59-col. 3, ll. 37]; examiner specifies that the disclosure repeatedly discusses contextual search queries/questions and semantic embeddings for content retrieval which could be interpreted as NL, additionally examiner specifies that both Bender and Tran explicitly teach NL and NLP);
generating, using a search model, in real-time, a contextualized embedding for the search query to form a search vector (utilizes contextual embeddings to perform search queries…feature vectors are a contextually-meaningful representation [col. 2, ll. 42-57], embedding operations or functions, including generating feature vectors representing document partitions and/or queries … [col. 5, ll. 12-37], [col. 5, ll. 55-col. 6, ll. 7], [col. 6, ll. 56-col. 7, ll. 28], a query vector representing the query [col. 9, ll. 64-col. 10, ll. 7], also see [col. 15, ll. 45-col. 16, ll. 58]), the search model is configured to split the electronic document into a plurality of information blocks (multi-step partitioning process to partition each document [col. 2, ll. 17-32], partition documents into sets of document partitions [col. 2, ll. 47-58], partitioning the document into a plurality of document partitions [col. 8, ll. 50-61]), wherein each information block in the plurality of information blocks includes a defined amount of textual information from the electronic document (system may select a size limit for the document partitions [col. 2, ll. 66-col. 3, ll. 25], partitioning process may have a size limit resulting in a word count that corresponds to a partition [col. 5, ll. 38-54], also see [col. 4, ll. 65-col. 5, ll. 11], [col. 12, ll. 13-28], [col. 12, ll. 48-64]) and is encoded into one or more contextualized embeddings (feature vectors are a contextually-meaningful representation [col. 2, ll. 47-58], embedding operations/functions [col. 5, ll. 12-37], generate content vectors [col. 8, ll. 61-col. 9, ll. 6], [col. 9, ll. 64-col. 10, ll. 7], content embedding [col.. 14, ll. 32-col. 15, ll. 4]) corresponding to one or more document vectors in a plurality of document vectors (feature vectors representing document partitions [col. 5, ll. 12-37], feature vectors may include a plurality of feature vectors that each represent a particular document partition [col. 8, ll. 15-25], content vectors for a respective document [col. 8, ll. 61-col. 9, ll. 6]).
However, Matei does not explicitly facilitates sending a request to a generative artificial intelligence (Al) model for an abstractive summary of the electronic document content, the request including the set of candidate document vectors, the abstractive summary to comprise a natural language representation of the electronic document content; and receiving the abstractive summary from the generative Al model.
Socher discloses, sending a request to a generative artificial intelligence (Al) model for an abstractive summary of the electronic document content, the request including the set of candidate document vectors, the abstractive summary to comprise a natural language representation of the electronic document content (In another embodiment, the generative AI platform may comprise a vision-language model such that the vision-language model may obtain and generate a summary of vision content (such as image or video content) from a searched web link, and use such summary as an input to generate a text answer in response to a user input ¶ [0022]-[0023], [0026]. For example, at least one NLP model 115 may be used to parse the web content following the links provided in search results 113a-n. In one embodiment, at least one NLP model 115 may generate a summary of the web content from at least one search result 113a, and use such generated summary to generate the final NL output 125 ¶ [0035], [0040]); and
receiving the abstractive summary from the generative Al model (In another embodiment, the generative AI platform may comprise a vision-language model such that the vision-language model may obtain and generate a summary of vision content (such as image or video content) from a searched web link, and use such summary as an input to generate a text answer in response to a user input ¶ [0022]-[0023], [0136]).
However, neither Matei nor Socher explicitly facilitate selecting a set of candidate document vectors from the plurality of document vectors that are semantically similar to the search vector by searching the inverted document index using the contextualized embedding for the search query; generating an interactive graphical user interface view including a plurality of graphical user interface elements representative of at least one of: the abstractive summary or one or more of the set of candidate document vectors.
Bender discloses, selecting a set of candidate document vectors from the plurality of document vectors that are semantically similar to the search vector by searching the inverted document index using the contextualized embedding for the search query (obtaining a set of text documents; for each respective document in the set of text documents: determining whether a pre-existing text summary in the text document is present in the respective document based on a header or whitespace separation between the pre-existing text summary and other text in the respective document; adding a respective sequence of n-grams of the respective pre-existing text summary to a set of training summaries; and performing a set of supervised learning operations to train a summarization model that represents n-grams of the set of text documents as vectors in an embedding space in which pairwise distances between vectors is indicative of semantic similarity of pairs of n-grams represented by respective pairs of vectors, wherein generating a text summary comprises using the summarization model ¶ [0372]. Also see ¶ [0077], [0201]),
generating an interactive graphical user interface view including a plurality of graphical user interface elements representative of at least one of: the abstractive summary or one or more of the set of candidate document vectors (Bender: FIG. 15 is an example user interface including an ontology-generated summary, in accordance with some embodiments of the present techniques. The UI 1500 shows a search bar 1510 displaying the query, “nursing mothers and benzoyl peroxide.” After an interaction with the UI element 1512, some embodiments may display a first search result box 1520 having a document summary 1522 and a second search result box 1530 having a document summary 1532 ¶ [0238]. FIG. 22 is a diagram of an example set of user interface elements indicating comparisons between different versions of a document, in accordance with some embodiments of the present techniques. The set of UI elements 2200 includes a change summary window 2210 and a text comparison window 2250. The change summary window 2210 includes a first summary window 2212 and a second summary window 2213. Each respective summary window of the first and second summary windows 2212-2213 summarizes both a total number of text sections and a count of text sections corresponding to ontology graph categories ¶ [00347]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Bender's system would have allowed Matei and Socher to facilitate selecting a set of candidate document vectors from the plurality of document vectors that are semantically similar to the search vector by searching the inverted document index using the contextualized embedding for the search query; generating an interactive graphical user interface view including a plurality of graphical user interface elements representative of at least one of: the abstractive summary or one or more of the set of candidate document vectors. The motivation to combine is apparent in the Matei and Socher reference, because there is a need to improve natural language processing for cross-context natural language model generation.
However, neither one of Matei, Socher or Bender explicitly facilitates the plurality of document vectors forming an inverted document index of the plurality of document vectors.
Tran discloses, the plurality of document vectors forming an inverted document index of the plurality of document vectors (Alternatively, the system can retrieve from a large knowledge base, instead of retrieving an initial dialogue utterance and then condition the generation on the retrieved knowledge. The same retrieval system uses a TF-IDF-based inverted index lookup over the collected/crawled data to produce an initial set of knowledge candidates ¶ [0231]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Tran's system would have allowed Matei, Socher and Bender to facilitate the plurality of document vectors forming an inverted document index of the plurality of document vectors. The motivation to combine is apparent in the Matei, Socher and Bender reference, because there is a need to improve automated content generation.
Regarding claims 2, 11 and 17, the combination of Matei, Socher, Bender and Tran discloses, comprising receiving the search query from a search box of the graphical user interface (GUI) on a web page or a click event on a GUI element on the web page (Socher: See Figs. 10A-E).
Regarding claims 3, 12 and 18, the combination of Matei, Socher, Bender and Tran discloses, wherein the electronic document is an unsigned electronic agreement with metadata comprising signature tag marker element (STME) information suitable to receive an electronic signature (Tran: The text includes ontology or semantic tags to aid a search engine in locating best matching responses that are in natural language ¶ [0010]. Also see ¶ [0097], [0099], [0332]).
Regarding claims 4, 13 and 19, the combination of Matei, Socher, Bender and Tran discloses, wherein the contextualized embedding comprises a vector representation of a sequence of words that includes contextual information for the sequence of words (Tran: The system is also context-aware, as it can capture information that describes the context of the hardware involved. Code similarity detects the similarity score between the input and any other implementation that has undergone the same mapping or transformation process. The resulting machine operation feature vector is provided to the learning machine ¶ [0072]. There are two approaches for the problem of sentiment analysis, either to use supervised machine learning or unsupervised lexicon-based approach. In one embodiment, semantic word vector spaces can be used in search query can be used where a vector generated from co-occurrence statistics of a word and its adjacent words is used to encode the meaning of this word. Although word vector models have succeeded to perform certain NLP tasks such as sentiment analysis, yet they neglect the compositionality, and context at which these words have been used. Thus, they produce misleading, and erroneous results at sentences where long dependencies exist such as sentences which include negation words or adverbs with similar meanings. Another drawback, word vectors obtained via co-occurrence statistics consider two factors: syntactic, and semantic similarity so if a small window of context has been used then words like bad, good have very similar representation ¶ [0098]).
Regarding claims 5, 14 and 20, the combination of Matei, Socher, Bender and Tran discloses, comprising training a bidirectional encoder representations from transformers (BERT) language model composed of multiple transformer encoder layers using training data from electronic documents associated with a defined entity and having an electronic signature (Tran: applying GPT (Generative Pre-trained Transformer) model or a BERT (Bidirectional Encoder Representations from Transformers) model to generate the text ¶ [0021], [0031], [0035], [0141], [0166], [0484]).
Regarding claims 6 and 15, the combination of Matei, Socher, Bender and Tran discloses, comprising generating the contextualized embeddings using a transformer architecture, the transformer architecture to comprise a bidirectional encoder representations from transformers (BERT) language model composed of multiple transformer encoder layers (Tran: Tranformer models such as GPT or Bidirectional Encoder Representations from transformers (BERT) ar applied to the training OA data to predict whether a set of claims is likely to face 101 rejections [0484]. FIG. 4A shows top level views of the GPT, BERT, and Transformer architectures with a token bias process to provide context sensitive short or long form text generation ¶ [0336]).
Regarding claim 7, the combination of Matei, Socher, Bender and Tran discloses, generating the contextualized embeddings using a bidirectional encoder representations from transformers (BERT) language model; indexing the contextualized embeddings for the electronic document to form the inverted document index; and storing the inverted document index in a database (Tran: generating context-sensitive text by: training a learning machine architecture (LMA) a corpus on a specific domain (such as engineering, medical, chemical, patent), wherein the architecture can be BERT, GPT, or a suitable network ¶ [0030]-[0037]. FIG. 4A shows top level views of the GPT, BERT, and Transformer architectures with a token bias process to provide context sensitive short or long form text generation ¶ [0336]. Another embodiment generates context-sensitive text by: using a first learning machine to map text matching each topic to a corresponding vector; building a search index for the search topics and in response to a search topic returning a responsive first vector ¶ [0171]-[0173]).
Regarding claim 8, the combination of Matei, Socher, Bender and Tran discloses, wherein the contextualized embeddings include at least one of: a word level vector, a sentence level vector, a paragraph level vector, or any combination thereof (Tran: To put the sequence of words in account, the Skip-Thought sentence encoder is used with two parts, an encoder and a decoder. The encoder part is a GRU-RNN which generate a fixed length vector for each sentence. The decoder part takes the vector representation as an input and tries to generate two sentences (the next and the previous to it) ¶ [0076]. Also see ¶ [0098], [0105], [0338]-[0341]).
Regarding claim 9, the combination of Matei, Socher, Bender and Tran discloses, comprising retrieving the set of candidate document vectors that are semantically similar to the search vector using a semantic ranking algorithm (Tran: The embodiment captures information that describes the context of the code (e.g., it is a function call, it is an operation, etc.). 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 ¶ [0070]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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5/28/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
Prosecution Insights