DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 3, 6, 16, and 19 are objected to because of the following informalities:
Claims 3 and 16 set forth a limitation of “performing vectorization processing on each sentence in the to-be-processed text to determine a vectorization result for the sentence”, but “a vectorization result” is already set forth by independent claims 1 and 13. Here, antecedent basis is not proper for “a vectorization result” because it is unclear that a same prior-recited limitation is being referenced. Generally, any initial recitation of a limitation should be accompanied by an indefinite article of “a” or “an”, but any subsequent recitation of the same limitation should be accompanied by a definite article of “the” or “said”. Applicants can overcome this objection by amending claims 3 and 16 to change “a vectorization result” to “the vectorization result”.
Claims 6 and 19 set forth “wherein integrating the position similarity and text similarity to obtain an integrated similarity”, but “an integrated similarity” is already set forth by independent claims 1 and 13. Here, antecedent basis is not proper for “an integrated similarity” because it is unclear that a same prior-recited limitation is being referenced. Generally, any initial recitation of a limitation should be accompanied by an indefinite article of “a” or “an”, but any subsequent recitation of the same limitation should be accompanied by a definite article of “the” or “said”. Applicants can overcome this objection by amending claims 6 and 19 to change “an integrated similarity” to “the integrated similarity”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 5, 6, 13, 15, 18, 19, and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (U.S. Patent Publication 2021/0407499) in view of Boni et al. (U.S. Patent No. 10,984,168).
Concerning independent claims 1, 13, and 31, Sun et al. discloses a method, system and computer program product for automatically generating conference minutes, comprising:
“obtaining a to-be-processed text” -- conference minutes generation comprises acquiring a text conference record (Abstract); minutes generation includes acquiring a text conference record (¶[0022] - ¶[0023]: Figure 1: Step 101);
“performing minutes extraction on the to-be-processed text based on one or more minutes categories to determine a minutes sentence belonging to each of the one or more minutes categories” – a text conference record is divided into a plurality of paragraphs (Abstract); generating a conference paragraph summary comprises evaluating each sentence in a conference paragraph to obtain an evaluation of the sentence (¶[0035]: Figure 4: Step 10221); different topic entities may be extracted from a text conference record based on topic entities that are identified; based on a same topic entity, opinions and attitudes expressed by several participant entities on the topic entity are obtained (¶[0062] - ¶[0063]); keywords and high-frequency words from a text conference record are extracted by performing semantic calculation on the text conference record, and conference topics are determined based on the keywords and high-frequency words; words may comprise categories generated; keywords and high-frequency words representing conference topics may be determined by screening sentences containing the keywords and high-frequency words; semantic calculation on the text record may be performed so that content that users are interested in can be extracted (¶[0069]); here, topics of a conference minutes record are “one or more minutes categories”, and sentences are analyzed to determine a topic for a sentence based on keywords and high-frequency words in a sentence (“determine a minutes sentence belonging to each of the one or more minutes categories”);
“determining a text similarity between each sentence in the to-be-processed text and the minutes sentence in the to-be-processed text based on a vectorization result of the sentence in the to-be-processed text and a vectorization result of the minutes sentence in the to-be-processed text” – generating a conference paragraph summary for each conference paragraph comprises evaluating each sentence in the conference paragraph to obtain an evaluation value of the sentence to form a candidate sentence set; evaluating each sentence in the conference paragraph to obtain an evaluation value may comprise calculating a score value (¶[0035] - ¶[0036]: Figure 3); a neural network is used for obtaining a score value of the sentence by adding the entire sentence information to an output vector expression (“a vectorization result”) (¶[0038]); features of conference instructions are calculated through word vector calculation, and features that are semantically similar; whether certain words and the features of conference instructions have similarity is determined through word vector calculation, and if a determined result is that similarity exists, all words having the similarity are identified in the same conference instruction (”determining a text similarity”) (¶[0068]);
“determining another sentence associated with the minutes sentence from the to-be-processed text based on the [integrated] similarity, and storing an association relationship between the minutes sentence and the another sentence” – candidate sentences are determined according to an evaluation value of each sentence in the conference paragraph to form a candidate sentence set; a conference paragraph summary is generated based on the candidate sentence set (¶[0035]: Figure 4: Steps 10222 to 10223); a similarity is calculated between a sentence and other sentences in a paragraph; a coherence value is obtained by comparing the correlation between the sentence and other sentences in the paragraph, and a higher coherence value indicates that the relationship between the sentence and the other sentences in the paragraph is closer; a coherence value is calculated by comparing a coherence degree of a correlation between the sentence and the other sentences in the paragraph (¶[0040] - ¶[0044]); here, determining a coherence of a sentence and other sentences in the same paragraph is “determining another sentence associated with the minutes sentence from the to-be-processed text”; a conference paragraph summary may be generated based on the candidate sentence set; a conference paragraph summary may be generated by directly arranging the candidate sentences in order (¶[0049]); candidate summary sentences are determined according to an evaluation value of each summary sentence in the conference paragraph summary to form a candidate summary sentence set, and a conference record summary is generated based on the candidate summary sentence set (¶[0051]); broadly, “storing an association relationship between the minutes sentence and the another sentence” is performed by associating sentences in a generated conference summary; that is, an association is ‘stored’ for all coherent sentences in a sentence set of a summary by associating the sentences that belong together in the summary.
Concerning independent claims 1, 13, and 31, Sun et al. discloses performing minutes extraction based on one or more minutes categories corresponding to “topics”, and determining a text similarity of sentences based on a vectorization of sentences. However, Sun et al. omits “determining a position similarity between a position of each sentence in the to-be-processed text and a position of the minutes sentence in the to-be-processed text” and “integrating the position similarity and the text similarity to obtain an integrated similarity” so that determining another sentence associated with a minutes sentence is “based on the integrated similarity”.
Concerning independent claims 1, 13, and 31, Boni et al. teaches generating a multi-modal abstract by extracting a set of representative elements from a set of textual descriptions from a document having a highest score computed by applying a scoring function indicative of a degree to which the set of representative elements represents the document. (Abstract) Here, generating an abstract of a document is analogous to generating minutes of a conference. Applying a score function to the set of representative text elements comprises computing at least one similarity score (“a text similarity”) indicative of a degree of relevance of the set of representative text elements to a text of the digital document, and a position score (“a position similarity”) indicative of a degree by which the set of representative text elements appear at the digital document’s beginning. Applying the score function to the set of representative text elements comprises computing a product of the similarity score and the position score (“integrating the position similarity and the text similarity to obtain an integrated similarity”). (Column 3, Lines 39 to 60) Compare claims 6 and 19, which state that “integrating the position similarity and the text similarity to obtain an integrated similarity comprises: obtaining a product of the position similarity and the text similarity as the integrated similarity. . . .” Applying a score function to the candidate set of elements comprises computing a similarity score indicative of a degree of relevance of the candidate set of elements to the text of the digital document (“determining a text similarity between each sentence in the to-be-processed text and the minutes sentence in the to-be-processed text”). (Column 12, Line 54 to Column 13, Line 10) Applying the score function to the candidate set of elements comprises computing a position score indicative of a degree by which the candidate set of elements appears in the digital document’s beginning (“determining a position similarity between a position of each sentence in the to-be-processed text and a position of the minutes sentence in the to-be-processed text”). A set having elements appearing close to the beginning of the digital document has a high position score. A position score is computed with k denoting a text element of a candidate set of elements K. Here, a position of k denotes an amount of characters between a beginning of the text of the digital document and a first appearance of k in the text, and k denotes a text element of the candidate set of elements. (Column 13, Line 53 to Column 14, Line 6) Applying a score function to the candidate set of elements comprises computing a product of the similarity score and the position score. (Column 14, Lines 21 to 24) Boni et al., then, teaches determining candidate elements from a text document to be included in an abstract of the text document using an integrated product of a text similarity and a position similarity. An objective is to generate an abstract of a document, e.g., an academic article, that briefly summarizes the document to help a reader quickly ascertain the purpose of the document, to give the reader an immediate understanding of the document, and to quickly determine whether the document is relevant to the reader’s interest at a glance without having to read through the entire document. (Column 1, Lines 6 to 57) It would have been obvious to one having ordinary skill in the art to integrate a position similarity and a text similarity of a document to generate an abstract of a document as taught by Boni et al. to automatically generate conference minutes in Sun et al. for a purpose of giving a reader an immediate understanding of a document so as to quickly determine whether the document is relevant to the reader’s interest.
Concerning claims 2 and 15, Sun et al. discloses that keywords and high-frequency words from a text conference record are extracted by performing semantic calculation on the text conference record, and conference topics are determined based on the keywords and high-frequency words; words may comprise categories generated; keywords and high-frequency words representing conference topics may be determined by screening sentences containing the keywords and high-frequency words; semantic calculation on the text record may be performed so that content that users are interested in can be extracted (¶[0069]). Here, determining conference topics is “at least one of . . . a text topic category.”
Concerning claims 5 and 18, Sun et al. discloses comparing a sentence and other sentences according to a semantic similarity (¶[0040]); keywords and high-frequency words from a text conference record are extracted by performing semantic calculation on the text conference record, and conference topics are determined based on the keywords and high-frequency words; words may comprise categories generated; keywords and high-frequency words representing conference topics may be determined by screening sentences containing the keywords and high-frequency words; semantic calculation on the text record may be performed so that content that users are interested in can be extracted (¶[0069]). Here, determining that sentences of conference minutes are semantically similar to topics is “text matching on the to-be-processed text based on category indications” and “determining the minutes sentence belonging to the one or more minutes categories according to the text matching.” That is, keywords and high-frequency words of sentences are ‘matched’ to topics, or “categories”.
Concerning claims 6 and 19, Boni et al. teaches that applying the score function to the set of representative text elements comprises computing a product of the similarity score and the position score (“integrating the position similarity and the text similarity to obtain an integrated similarity comprises: obtaining a product of the position similarity and the text similarity as the integrated similarity . . .”). (Column 3, Lines 39 to 60) Applying a score function to the candidate set of elements comprises computing a product of the similarity score and the position score. (Column 14, Lines 21 to 24)
Claims 3 to 4 and 16 to 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (U.S. Patent Publication 2021/0407499) in view of Boni et al. (U.S. Patent No. 10,984,168) as applied to claims 1 and 13 above, and further in view of Singh Bawa et al. (U.S. Patent Publication 2022/0237373).
Sun et al. discloses that a score value of a sentence is calculated through a neural network, and the entire sentence information may be added to an output vector expression based on sentence information. (¶[0037] - ¶[0038]) Sun et al., then, discloses “performing vectorization processing on each sentence in the to-be-processed text to obtain a vectorization result for the sentence”. Additionally, Sun et al. discloses “determining the minutes sentence corresponding to each of the one or more minutes categories” by determining categories and conference topics. (¶[0069]) However, Sun et al. does not expressly disclose the limitations of “inputting the vectorization result into a pre-trained text recognition model” so that determining a minutes sentence is “according to an output result of the text recognition model, the text recognition model being configured to recognize whether the sentence belongs to one of the one or more minutes categories.” Additionally, Sun et al. does not expressly disclose the limitations of “wherein the text recognition model is a machine learning model, a training sample of the machine learning model being a plurality of sentences having category indications and each of the category indications indicating a minutes category to which a corresponding sentence belongs.” That is, Sun et al. does not expressly disclose that determining a sentence to be included in minutes and a category of a sentence is performed with “a pre-trained text recognition model” that is “a machine learning model” trained with “a training sample of the machine learning model being a plurality of sentences having category indications”. Still, Sun et al. appears to disclose that selecting sentences to be included in minutes and determining categories of sentences are performed with a neural network, and this neural network could be described as “a text recognition model” and “a machine learning model”. Conventionally, neural networks are trained with labeled training samples, so that a neural network that generates categories and topics for sentences might generally be trained with training samples labeled by category in Sun et al.
Singh Bawa et al. teaches automated categorization and summarization of documents using machine learning to select a document category of a plurality of document categories. (Abstract) A first set of ML models may be trained (“wherein the text recognition model is a machine learning model”) to determine underlying similarities and differences between different categories of documents based on word features from labeled documents of different categories (“a pre-trained recognition model . . . the text recognition model being configured to recognize whether the sentence belongs to one of the one or more [minutes] categories”). (¶[0005]) The various models may be trained using training data that is based on labeled and annotated documents of each of the predefined categories. (¶[0021]) Training engine 126 may generate training data 110 based on labeled, e.g., categorized, documents from databases 142 (“a training sample of the machine learning model being a plurality of sentences having category indications indicating a [minutes] category to which a corresponding sentence belongs”). (¶[0026]) An objective is to leverage machine learning and artificial intelligence to categorize and summarize various categories of documents. Categorizer 128 may be configured to perform vectorization on an input document including sentencization and term frequency-inverse document frequency (TF-IDF) vectorization (“performing vectorization processing on each sentence in the to-be-processed text to determine a vectorization result for the sentence”). (¶[0028]: Figure 1) (¶[0001]) It would have been obvious to one having ordinary skill in the art to provide training samples having category indications to train a machine learning model as taught by Singh Bawa et al. to categorize sentences using a neural network of Sun et al. for a purpose of leveraging machine learning and artificial intelligence to categorize and summarize categories of documents.
Claims 24 to 30 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (U.S. Patent Publication 2021/0407499) in view of Boni et al. (U.S. Patent No. 10,984,168) as applied to claims 1 and 13 above, and further in view of Meretab (U.S. Patent Publication 2011/0016416).
Concerning claims 24 and 28, Sun et al. discloses generating a conference summary of a plurality of sentences, but does not provide for display and selection of alternative minutes sentences for the summary in the limitations of “displaying the minutes category and the one or more minutes sentences corresponding to the minutes category” and “displaying, in response to detecting an association display instruction corresponding to a first minutes sentence among the one or more minutes sentences, another minutes sentence in the to-be-processed text associated with the first minutes sentence, based on the first minutes sentence and the stored association relationship between the first minutes sentence and the another sentence.”
Concerning claims 24 and 28, Meretab teaches storing and retrieving individual sentences on an interactive display so that relevant sentences from a single source or different sources can be retrieved, aggregated, and displayed along with context specifically tailored for each user-relevant sentence. (Abstract) Based on source material, subunits of sentences can be indexed. (¶[0050]) Sentences can be distinguished by sentence boundary disambiguation (SBD) techniques. (¶[0054]) An end user could first request from an article all identifier-associated sentences containing the word ‘gain’, and that request would designate sentences 226 and 236, or a user may start with all identifier-associated sentences containing the word ‘future’, and request associated sentences 228 and 234. (¶[0057]: Figure 2A) Indexed sentences can be stored in a record. (¶[0081]) Specifically, given a displayed sentence, a sentence navigation bar is provided that displays one or more additional sentences based on a defined relation between the displayed sentence and the additional sentences. Each relation can be associated with an icon that gets triggered by an event, e.g., actions performed by a user of a mouse click. A computer designates sentences based on relations so that designated sentences can be retrieved, displayed, and/or stored. Given an initial sentence and a specific relation of interest, a computer system may designate the sentences that meet the criteria of the relation. (¶[0083] - ¶[0085]) An event on a navigation bar, e.g., a mouse click, mouse over, or touch on a touch screen, causes additional sentences to be displayed. (¶[0091]) Figures 3 to 9 of Meretab illustrate displaying a predetermined category of ‘dividend’ and a sentence in a predetermined category of ‘dividend’ (‘We paid out approximately . . .’) (“displaying the [minutes] category and the one or more [minutes] sentences corresponding to the [minutes] category”), and then in response to a mouse click by a user (“an association display instruction”), displaying another sentence that meets criteria of a relation (“the stored association relationship between the first [minutes] sentence and the another sentence”) so that another sentence relating to ‘dividend’ is displayed (‘As Dave stated . . .’) (“displaying, in response to detecting an association display instruction corresponding to a first [minutes] sentence among the one or more [minutes] sentences, another sentence in the to-be-processed text associated with the first [minutes] sentence, based on the first [minutes] sentence and the stored association relationship between the first [minutes] sentence and the another sentence”). An objective is to collect and display information that enables a user to access information on a topic and read it coherently from across documents or from different locations within a document while enabling immediate access to surrounding content that may be required for further understanding. (¶[0003]) It would have been obvious to one having ordinary skill in the art to display another sentence that has a relationship to a sentence in a category pursuant to an association display instruction from a user as taught by Meretab to generate a conference record summary in Sun et al. for a purpose of enabling user access to information on a topic from different documents or within a same document to further understanding from surrounding content.
Concerning claims 25 to 26 and 29 to 30, Meretab teaches that, given a displayed sentence, a sentence navigation bar is provided that displays one or more additional sentences based on a defined relation between the displayed sentence and the additional sentences. Each relation can be associated with an icon that gets triggered by an event, e.g., actions performed by a user of a mouse click. A computer designates sentences based on relations so that designated sentences can be retrieved, displayed, and/or stored. Given an initial sentence and a specific relation of interest, a computer system may designate the sentences that meet the criteria of the relation. (¶[0083] - ¶[0085]) An event on a navigation bar, e.g., a mouse click, mouse over, or touch on a touch screen, causes additional sentences to be displayed. (¶[0091]) Here, a mouse click to trigger display of another sentence which meets specific criteria of a relationship is “a sentence triggering operation” on a target sentence and “a control triggering operation on an association display control” at the target sentence to determine “an instruction corresponding to the sentence triggering operation as the association display instruction” and “an instruction corresponding to the control triggering operation as the association display instruction.” That is, a mouse click causing additional related sentences to be displayed is “a sentence triggering operation” and “a control triggering operation” because a mouse click triggers control of additional associated sentences to be displayed.
Concerning claim 27, Meretab teaches at least “wherein the displaying the another sentence associated with the first [minutes] sentence comprises: displaying the another sentence associated with the first [minutes] sentence at a first position of the first [minutes] sentence” as illustrated in Figures 3 to 9 with an additional sentence being positioned above or below a given sentence on the graphical user interface, “and/or prominently displaying the another sentence associated with the first [minutes] sentence in the to-be-processed text” because color can be used to illustrate a relationship between originally displayed sentences in red and incrementally displayed sentences in green. (¶[0100]) That is, displaying a given sentence and another sentence in different colors provides for “prominently displaying the another sentence associated with the first [minutes] sentence”.
Response to Arguments
Applicants’ arguments filed on 07 November 2025 have been considered but are moot in view of new grounds of rejection as necessitated by amendment.
Applicants amend independent claims 1, 13, and 31 to set forth some significant new limitations of “determining a position similarity between a position of each sentence in the to-be-processed text and a position of the minutes sentence in the to-be-processed text”, “determining a text similarity between each sentence in the to-be-processed text and the minutes sentence in the to-be-processed text based on a vectorization result of the sentence in the to-be-processed text and a vectorization result of the minutes sentence in the to-be-processed text”, and “integrating the position similarity and the text similarity to obtain an integrated similarity”. Additionally, Applicants amend dependent claims 6 and 19 to set forth new limitations of “wherein integrating the position similarity and the text similarity to obtain an integrated similarity comprises: obtaining a product of the position similarity and the text similarity as the integrated similarity; or obtaining a weighted sum of the position similarity and the text similarity as the integrated similarity”. Applicants provide some additional amendments of an editorial nature. Then Applicants present some brief arguments traversing the prior rejection of the independent claims as being obvious under 35 U.S.C. §103 over Sun et al. (U.S. Patent Publication 2021/0407499) in view of Choi et al. (U.S. Patent No. 12,205,024). Applicants observe that Sun et al. discloses a coherence value that indicates a relationship between a sentence and another sentence in a paragraph, and states, “The two sentences to be compared may or may not be adjacent.” Applicants argue that Sun et al. obtains a coherence between two sentences that are adjacent or not adjacent, and that this implies that a coherence is obtained regardless of whether the two sentences are adjacent. Additionally, Applicants argue that Sun et al. fails to disclose the new limitations of an integrated similarity which is determined by integrating the text similarity and the position similarity between a position of each sentence in the to-be-processed text and a position of a minutes sentence in the to-be-processed text.
Applicants’ amendments overcome the objections to the drawings, and a replacement drawing is being approved.
Applicants’ amendments overcome the objections to the title and the Specification.
Applicants’ amendments necessitate some new claim objections because they set forth some limitations without proper antecedent basis.
Applicants’ arguments are moot given new grounds of rejection as necessitated by amendment. Independent claims 1, 13, and 31 are now rejected as being obvious under 35 U.S.C. §103 over Sun et al. (U.S. Patent Publication 2021/0407499) in view of Boni et al. (U.S. Patent No. 10,984,168). The rejection no longer relies upon Choi et al., as the new limitations are addressed by Boni et al. The rejection of certain dependent claims continues to rely upon Singh Bawa et al. (U.S. Patent Publication 2022/0237373) and Meretab (U.S. Patent Publication 2011/0016416).
Generally, independent claims 1, 13, and 31, as amended, are maintained to be obvious under 35 U.S.C. §103 over Sun et al. in view of Boni et al. Specifically, Boni et al. is maintained to clearly teach the new limitations of “determining a position similarity”, “determining a text similarity”, and “integrating the position similarity and the text similarity to obtain an integrated similarity” to determine sentences for generating an abstract of a document. Boni et al. teaches generating an abstract of a document that is analogous to generating minutes that summarize a conference in Sun et al. Both Sun et al. and Boni et al. includes various scoring strategies for generating a summary. Sun et al. discloses that a neural network may generate an output vector expression for a sentence so as to represent sentences as vectors and to provide “a vectorization result”. (¶[0037] - ¶[0038]) Additionally, Sun et al. discloses generating a summary based on categories or topics of the conference. (¶[0069]) Boni et al. teaches a similarity score that determines a degree of relevant of a candidate set of elements, e.g., a set of sentences, to text of a digital document. (Column 12, Line 54 to Column 13, Line 10) Boni et al.’s candidate set of elements, which is the set of elements to be incorporated into the abstract, corresponds to “the minutes sentence in the to-be-processed text” and text of a digital document corresponds to “the to-be-processed text”. Additionally, Boni et al. teaches “integrating” a position score and a similarity score by computing a product of a similarity score and a position score. (Column 3, Lines 57 to 60 and Column 14, Lines 21 to 24)
Applicants’ argument directed to how to interpret a disclosure that two sentence may or may not be adjacent is not completely persuasive, but this argument is moot given the new grounds of rejection. Arguably, Sun et al.’s disclosure, at ¶[0042], that two sentence may or may not be adjacent can be construed to consider both circumstances, i.e., sentences that are adjacent and sentences that are not adjacent, and this is not necessarily equivalent to not considering a position of the two sentences being adjacent at all as argued by Applicants.
However, “determining a position similarity between a position of each sentence in the to-be-processed text and a position of the minutes sentence in the to-be-processed text” is maintained to be taught by Boni et al. Here, Boni et al.’s “to-be-processed text” corresponds to the text of the document from which the abstract is to be generated, but this “to-be-processed text” may include text of an abstract that summarizes the document insofar as a summarizing abstract may include at least some of the text of the document. Boni et al., then, clearly teaches determining a position score of “a position of each sentence in the to-be-processed text” because “the to-be-processed text” is the text of the document. If a sentence is at the beginning of a document, then a sentence is given a higher score in “the to-be-processed text”. Additionally, Boni et al.’s ‘position of k’ in Equation (5) is defined for a text element k of a candidate set of elements K, and this candidate set of elements represents candidate text segments for an abstract that is to be generated. Consequently, Boni et al.’s position score is calculated based on a position of each element k of a candidate set of elements, i.e., “a position of the minutes sentence in the to-be-processed text”, and a position of k in the text of the document, i.e., “a position of each sentence in the to-be-processed text”. Broadly, if text of a document is at the beginning of a document, then it is more likely to be in the text summary of the document, and a candidate set of elements for the text summary is compared against a position of the text of the document (“the to-be-processed text”) to determine the position score in Equation (5) of Boni et al. Applicants’ Specification, ¶[0046] - ¶[0047], appears to be the only significant description of this embodiment.
All of the new grounds of rejection are necessitated by amendment. Accordingly, this rejection is properly FINAL.
Conclusion
Applicants’ amendment necessitated the new grounds of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicants are 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 MARTIN LERNER whose telephone number is (571) 272-7608. The examiner can normally be reached Monday-Thursday 8:30 AM-6:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARTIN LERNER/Primary Examiner
Art Unit 2658 December 18, 2025
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