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 .
DETAILED ACTION
This action is in response to the applicant's communication filed on 09/01/2022. In virtue of this communication, claims 8-20 as elected by applicant filled on 09/01/2022 are currently pending in the instant application.
Claims 1-7 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected embodiment, there being no allowable
,No generic or linking claim.
Information Disclosure Statement
The information Disclosure statement (IDS) form PTO-1449, filed on 12/31/2025 and 09/01/20222 are in compliance with the provisions of CFR 1.97. Accordingly, the information disclosed therein was considered by the examiner.
Drawings
The drawings were received on 09/01/2022 have been reviewed by Examiner and they are acceptable.
Response to Arguments
Applicant's arguments filed 12/08/2025 have been fully considered but they are not persuasive.
Applicant's election with traverse of Invention II, Claims 8-20 in the reply filed on 12/08/2025 is acknowledged. The traversal is on the ground(s) that the alleged inventions overlap in scope.
This is not found persuasive because Group I (Claims 1-7) is directed to a computer-implemented method performed by a collaborative analysis system that (i) obtains digital content from a data structure (ii) analyzes the digital content using first components (each using a respective machine-learning model) to determine plurality of types of information, stores plurality of types of information, analyze the stored data using second components, (v) determines one or more unintended interpretations, and importantly (vi) performs an action to prevent the unintended interpretations. In contract, Group II (claim 8-20) is directed to a computer program product (one or more computer-readable storage media with program instructions) for determining unintended interpretation, where the instructions include analyzing digital content using first components, determining the plurality of types of information, analyzing the plurality using a second component, determining one or more unintended interpretation and providing information regarding the unintended interpretation to a device, without requiring the method-side of modifying the digital content as recited in claim 1 and without using a machine learning model. Accordingly groups are distinct because the claimed subject matter differs in statutory class and scope (method that affirmatively modifies content versus a program product that at least provides information to a device), the inventions each have separate utility (a content modification workflow versus a distributable software product for outputting interpretation information). Furthermore, examining both groups would present a serious search and examination burden, because Group I requires search/examination focused on end-to-end operational workflows including content modification actions and machine learning models, while Group II requires search and examination focused on program product/CRM claim scope, detection and delivery of interpretation information to a device, which may implicate different prior art groupings and search strategies.
The requirement is still deemed proper.
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.
Claim 8 is rejected under 35 U.S.C. § 101 because the claims are directed to non-statutory subject matter in the form of a “computer program product.” The claims fall outside the scope of patent-eligible subject matter at least because the claimed computer program product is broad enough to encompass non-transitory embodiments. (E.g., one of ordinary skill in the art could reasonably be expected to interpret the claimed computer readable medium as a carrier wave onto which instructions could be coded.)
See also Mentor Graphics v. EVE-USA, Inc., 851 F.3d 1275, 112 USPQ2d 1120 (Fed. Cir. 2017) “Subject Matter Eligibility of Computer Readable Media” which states in relevant part “[i]n an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation ‘non-transitory’ to the claim.”
Therefore, an amendment applicable to the claims, consistent with the recommendations in the above-noted Mentor Graphics v. EVE-USA, Inc., that would overcome the instant ‘101 rejection, follows:
Claim 8 (Amended) A non-transitory computer program product …
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 8-9, 12, 15-19 is/are rejected under 35 U.S.C. 102(a)(2)as being anticipated by Deselaers et al. (US 2018/0137400).
As per claim 8, A computer program product for determining unintended interpretations of content, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: “program instructions to analyze digital content using first components of a collaborative analysis system;”(Deselaers, ¶[0024] discloses the systems and methods can receive and analyze a textual communication as the user types it into the composition interface.¶[0032] discloses the detection portion of the communication assistance model can include a feature extraction component that extracts one or more features of the communication to be analyzed. ¶[0044] discloses the context component can receive the identity of the author, the identity of the recipient, and/or other contextual information as inputs and, in response, output a context score. For example, the other contextual information can include a message thread that led to the current communication. )
“program instructions to determine a plurality of types of information regarding the digital content based on analyzing the digital content using the first components;”( Deselaers,¶[0032] discloses the detection portion of the communication assistance model can include a feature extraction component that extracts one or more features of the communication to be analyzed. ¶[0045] discloses the context score can include one or more numerical scores that indicate expected values for certain communication characteristics (e.g., tone, language, grammatical precision, etc.).)
“program instructions to analyze the plurality of types of information using a second component of the collaborative analysis system;”( Deselaers,¶[0046] discloses the context score can be input into the remainder of the detection portion of the communication assistance model (e.g., an LSTM recurrent neural network) along with the communication itself. The context score can assist the detection portion of the communication assistance model in determining which, if any, portions of the communication should be identified as problematic.)
“program instructions to determine one or more unintended interpretations of the digital content based on analyzing the plurality of types of information;”(Deselaers, ¶[0023] discloses the communication assistance model can include a long short-term memory recurrent neural network that detects an inappropriate tone or unintended meaning within a user-composed communication and provides one or more suggested replacement statements to replace the problematic statements. ¶[0034] discloses categories of problematic statements can include inappropriate language (e.g., offensive or derogatory terms or euphemisms); inappropriate tone (e.g., passive-aggressive tone or overly aggressive tone); statements that reveal confidential information; legally problematic statements; and/or an unintended meaning. Thus, training the detection portion of the communication assistance model (e.g., a deep LSTM recurrent neural network) on an appropriate training dataset can further enable the communication assistance model to not only recognize explicitly offensive words but also perform the more complex task of detecting more subtle problematic statements such as, for example, inappropriate tone, derogatory euphemisms, or biased language.
“and program instructions to provide information regarding the one or more unintended interpretations to a device.”( Deselaers,¶[0010] discloses The method includes providing, by the one or more computing devices, information regarding the one or more problematic statements for display to the user. ¶[0050] discloses the systems and methods of the present disclosure can notify the user to the existence of the problematic statement in a number of ways. As one example, a non-intrusive notification (e.g., a pop-up window or icon in a lower screen area) can be provided that simply notifies the user of the existence of the one or more problematic statements. If the user selects or otherwise engages with the notification, additional information (e.g., explicit identification of the problematic statements and/or the suggested replacement statements) can be displayed or otherwise provided. )
Claim 15 have been analyzed and is rejected for the reasons indicated in claim 8 above.
As per claim 9, The computer program product of claim 8, Deselaers further discloses “wherein the program instructions to determine the one or more unintended interpretations comprise: program instructions to analyze information identified by one or more types of information of the plurality of types of information; and program instructions to determine an unintended interpretation based on analyzing the information identified by the one or more types of information.”( Deselaers, ¶[0034] discloses As examples, categories of problematic statements can include inappropriate language (e.g., offensive or derogatory terms or euphemisms); inappropriate tone (e.g., passive-aggressive tone or overly aggressive tone); statements that reveal confidential information; legally problematic statements; and/or an unintended meaning. Thus, training the detection portion of the communication assistance model (e.g., a deep LSTM recurrent neural network) on an appropriate training dataset can further enable the communication assistance model to not only recognize explicitly offensive words but also perform the more complex task of detecting more subtle problematic statements such as, for example, inappropriate tone, derogatory euphemisms, or biased language. In additional implementations, the communication assistance model can detect when an individual composing a communication is intoxicated or otherwise incapacitated. )
As per claim 12, The computer program product of claim 8, “wherein the first components include an image recognition component, and wherein the program instructions to determine the plurality of types of information comprise: program instructions to determine a type of information regarding items identified by the digital content.”( Deselaers, ¶[0032] discloses the detection portion of the communication assistance model can include a feature extraction component that extracts one or more features of the communication to be analyzed. ¶[0045] discloses the context score can include one or more numerical scores that indicate expected values for certain communication characteristics (e.g., tone, language, grammatical precision, etc.). further see ¶[0074].)
As per claim 16, The system of claim 15, wherein, “to analyze the digital content, the one or more devices are configured to: analyze, using a first one of the first components of the system, the digital content; and analyze, using a second one of the first components of the system, the digital content and information generated based on the first one of the first components analyzing the digital content.” (Deselaers, ¶[0024] discloses the systems and methods can receive and analyze a textual communication as the user types it into the composition interface.¶[0032] discloses the detection portion of the communication assistance model can include a feature extraction component that extracts one or more features of the communication to be analyzed. ¶[0044] discloses the context component can receive the identity of the author, the identity of the recipient, and/or other contextual information as inputs and, in response, output a context score. For example, the other contextual information can include a message thread that led to the current communication. ¶[0046] discloses the context score can be input into the remainder of the detection portion of the communication assistance model (e.g., an LSTM recurrent neural network) along with the communication itself. The context score can assist the detection portion of the communication assistance model in determining which, if any, portions of the communication should be identified as problematic.)
As per claim 17, The system of claim 15, “wherein the one or more unintended interpretations are a plurality of unintended interpretations, and wherein the one or more devices are configured to: aggregate the plurality of unintended interpretations into a first group of unintended interpretations and a second group of unintended interpretations; and provide first information regarding first group of unintended interpretations and second information regarding the second group of unintended interpretations.”(Deselaers, ¶[0034] discloses categories of problematic statements can include inappropriate language (e.g., offensive or derogatory terms or euphemisms); inappropriate tone (e.g., passive-aggressive tone or overly aggressive tone); statements that reveal confidential information; legally problematic statements; and/or an unintended meaning. ¶[0035] discloses the communication assistance model can output one or more confidence scores respectively for the one or more problematic statements identified within the communication. The confidence score for each problematic statement can indicate a confidence that the identified statement is indeed problematic or can otherwise indicate a degree to which the identified statement is problematic. ¶[0036] discloses in some implementations, the confidence score for each identified problematic statement can be compared to a particular threshold value to determine whether to notify the user of the problematic statement or otherwise intervene. Thus, for example, the systems of the present disclosure can ignore a first problematic statement that has a confidence score below the threshold value, but notify the user regarding a second problematic statement that has a confidence score greater than the particular threshold value. Further see ¶[0037], ¶[0050] notify the user to the existence of the problematic statement. ¶[0054],¶[0118]. )
As per claim 18, The system of claim 17, “wherein, to provide the first information, the one or more devices are configured: determine that a measure of confidence, associated with the first group of unintended interpretations, satisfies a confidence threshold; and provide the first information based on determining that the measure of confidence satisfies the confidence threshold.”( Deselaers, ¶[0035] discloses the communication assistance model can output one or more confidence scores respectively for the one or more problematic statements identified within the communication. The confidence score for each problematic statement can indicate a confidence that the identified statement is indeed problematic or can otherwise indicate a degree to which the identified statement is problematic. ¶[0036] discloses in some implementations, the confidence score for each identified problematic statement can be compared to a particular threshold value to determine whether to notify the user of the problematic statement or otherwise intervene. Thus, for example, the systems of the present disclosure can ignore a first problematic statement that has a confidence score below the threshold value, but notify the user regarding a second problematic statement that has a confidence score greater than the particular threshold value. ¶[0037] discloses the systems of the present disclosure can provide a non-intrusive notification for a first problematic statement that has a confidence score that is less than a particular threshold value (but greater than a base threshold value), but can automatically replace a second problematic statement (e.g., with a suggested replacement statement) that has a confidence score greater than the particular threshold value. )
As per claim 19, The system of claim 17, “wherein, to provide the first information, the one or more devices are configured: determine a first measure of confidence associated with the first group of unintended interpretations; determine a second measure of confidence associated with the second group of unintended interpretations; rank the first information and the second information based on the first measure of confidence and the second measure of confidence; and provide the first information and the second information based on ranking the first information and the second information.”( Deselaers, ¶[0037] discloses the systems of the present disclosure can provide a non-intrusive notification for a first problematic statement that has a confidence score that is less than a particular threshold value (but greater than a base threshold value), but can automatically replace a second problematic statement (e.g., with a suggested replacement statement) that has a confidence score greater than the particular threshold value. ¶[0038] discloses the threshold values for notification/intervention can be a function of a context associated with the communication. In particular, as one example, the threshold values for notification/intervention in a professional communication context may be relatively less than the threshold values for notification/intervention in a casual, light-hearted context. Thus, the systems and methods of the present disclosure can adjust various thresholds based on a determined context. ¶[0054],¶[0118].)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deselaers et al. (US 2018/0137400), in view of Vogel et al. (US 20140108006).
As per claim 10, The computer program product of claim 8, Deselaers does not explicitly disclose the following which would have been obvious in view of Vogel from similar field of endeavor “wherein the first components include a semiotic component,” and wherein the program instructions to determine the plurality of types of information comprise: “program instructions to analyze, using the semiotic component, objects identified by one or more of the plurality of types of information; and program instructions to determine a semiotic meaning of the objects, wherein the plurality of types of information includes a type of information identifying the semiotic meaning.”(Vogel,¶[0008-0009] disclose s identifying the semiotic attributes of the documents, such as writing style or genre and writing tone or sentiment, ¶[0068-0069] discloses semiotic analysis and mapping engine are part of the system components. ¶[0110] discloses an isotopy is longitudinal study of topic markers, i.e., a correlational study that involves repeated observations of the same semiotic markers across a series of utterances. These markers define the isotopy code, which in turn will define the semiotic persona of an extracted entity. ¶[0112] discloses the document is collected and then analyzed through dependency grammar parsing, extracting entities and isotopies to form entity semiotic personas. This document will be indexed according to the semiotic personas of entities contained in the document. Further see ¶[0113-0114] discloses This type of parsing surfaces narrative dependencies that will characterize isotopies, which are then used to create a semiotic persona for a given entity contained within an article or document. Creation of an entity persona allows for comparison between a plurality of entities through the mapping of features belonging to each persona. Here, dependency grammar parsing starts by identifying the verbs in each sentence, then identifying entities, arguments and functions attached to the verb. These entities, arguments and functions define features of the entities contained in the article and they are used in the entity map to be leveraged into isotopies. The narrative functions, entities and verbs surfaced through dependency grammar parsing demonstrated in FIG. 24 are listed in FIG. 25. These functions are extracted from the sample text and then mapped with their corresponding entity in order to gauge the semiotic distance between two given entities. Entities that share features will be clustered more closely together on the map. The isotopies across the narrative functions identified here are illustrated in FIG. 26. These extracted narrative functions are grouped by their semiotic markers in order to form different isotopy patterns.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Vogel technique of analyzing content semiotic relationships into Deselaers technique to provide the known and expected uses and benefits of Vogel technique over detecting problematic statement technique of Deselaers. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Vogel to Deselaers in order to enhance a standard recommendation with higher degree of relevancy. (Refer to Vogel paragraphs [0006-0007].)
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deselaers et al. (US 2018/0137400), in view of Crouch et al. (US 20160078102).
As per claim 11, The computer program product of claim 9, Deselaers does not explicitly disclose the following which would have been obvious in view of Crouch from similar field of endeavor “wherein the program instructions further comprise program instructions to obtain the digital content from a knowledge base; and program instructions to store the plurality of types of information in the knowledge base, wherein the plurality of types of information, analyzed using the second component, are obtained from the knowledge base.”(Crouch, ¶[0037] discloses document preprocessing engine 214 may scan documents for indexing by communicating with such documents located in knowledge base 212 over network 230. Alternatively, document preprocessing algorithm may retrieve any metadata or annotation files associated with documents in knowledge base 212 over network 230 to QA system 210. ¶[0041]discloses document processing engine 214 may parse through each document of the set of documents in knowledge base 212 to generate sets of annotations per document. Such annotations may include text from section headings, document title, concept identifiers for subjects discussed in the passage. ¶[0042] discloses passage analyzer 218 may analyze metadata or annotations describing documents in knowledge base 212 to identify which documents should be included in the passage search. ¶[0060] discloses document preprocessor 320 may also identify the relevant annotations for each of these keywords, as described above, and store such identified annotations in entries of corresponding keywords in the passage index 360. ¶[0064] discloses a passage analyzer may execute a passage search by simultaneously searching through passage index 360, whether generated directly or indirectly from source document 310, along with metadata file 350. For each passage index entry that document preprocessor 320 parses through, the passage analyzer may search through metadata file 350 to identify how the keywords in the given passage index entry relate to the rest of the document by analyzing the document context that may be found in metadata file 350 for portions of the passage text. )
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Crouch technique of analyzing content of documents into Deselaers technique to provide the known and expected uses and benefits of Crouch technique over detecting problematic statement technique of Deselaers. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Crouch to Deselaers in order to accurately identify and analyze the relation between different part of document parts. (Refer to Crouch paragraphs [0003].)
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deselaers et al. (US 2018/0137400), in view of Huang et al. (US 2019/0026253).
As per claim 13, The computer program product of claim 12, Deselaers does not explicitly disclose the following which would have been obvious in view of Huang from similar field of endeavor “wherein the first components include a layout component, and wherein the program instructions to determine the plurality of types of information comprises: program instructions to determine a type of information that identifies geometric associations between the items identified by the digital content or geometric oppositions between the items identified by the digital content.” (Huang, ¶[0026] discloses the layout of the document based on the calculated features, as well as spatial and grammatical constraints (operation 118). As described further below with reference to FIG. 3, the layout specifies locations of content in the document. Moreover, determining the layout may involve constraint-based optimization based on the spatial constraints and the grammatical constraints. Furthermore, determining the layout may involve calculating a distance metric (such as a Mahalanobis distance metric and, more generally, a distance metric that takes into account correlations in the calculated features and that is scale-invariant) based on the spatial constraints and the grammatical constraints. ¶[0027] discloses hen a first box having the title “box 1” is identified, it may be known that a second box entitled “box 2” is located to the right of box 1 in several documents in the set of documents. In this way, the spatial and the grammatical constraints may be used to uniquely identify the document and its associated layout. ¶[0034], ¶[0043] discloses the document includes boxes (or fields), associated text (or titles), and content in at least some of boxes. Note that there are spatial relationships (such as relative positions) between boxes, and between text and boxes. These specify spatial constraints associated with income-tax document. Similarly, there are grammatical constraints on text in income-tax document.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Huang technique of analysis of an image of a document into Deselaers technique to provide the known and expected uses and benefits of Huang technique over detecting problematic statement technique of Deselaers. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Huang to Deselaers in order to provide better verification of a document. (Refer to Huang paragraphs [0005].)
Claim(s) 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deselaers et al. (US 2018/0137400), in view of Mesheryokov et al. (US 2015/0057991).
As per claim 14, The computer program product of claim 8, Deselaers does not explicitly disclose the following which would have been obvious in view of Mesheryokov from similar field of endeavor “wherein the program instructions to analyze the plurality of types of information comprise: program instructions to perform a linguistic analysis of information identified by one or more types of information of the plurality of information;”(Mesheryokov, ¶[0003] discloses analyzing, using one or more processors, a sentence of a first text to determine syntactic relationships among generalized constituents of the sentence, forming a graph of the generalized constituents of the sentence based on the syntactic relationships and a lexical-morphological structure of the sentence, analyzing the graph to determine a plurality of syntactic structures of the sentence. ¶[0037]. )
“and program instructions to determine a semantic meaning of a concept associated with the information identified by the one or more types of information,”( Mesheryokov, ¶[0096], disclose Lexical descriptions include a lexical-semantic dictionary, which includes a set of lexical meanings. The lexical meanings, along with their semantic classes, form a semantic hierarchy where each lexical value may include its reference to a language-independent semantic parent (i.e. the location in the semantic hierarchy), and its semantic value. Each lexical value may also attach various derivatives (such as words, expressions and phrases) that express meaning using various parts of speech, various forms of a word, words with the same root, etc. )
“wherein the unintended interpretation is based on the semantic meaning.”( Mesheryokov,¶[0003] discloses determining a semantic ambiguity in the sentence based on a difference between the first and second semantic structures. ¶[0099] discloses a syntactic ambiguity means that there are several probable syntactic structures at the syntactic analysis stage. Likewise, a semantic ambiguity means that there are several probable semantic structures at the semantic analysis stage.¶[0102] discloses If it is found that there are several different syntactic (semantic) structures with high overall ratings, it may be assumed that there is a syntactical (semantic) ambiguity in the text. )
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Mesheryokov technique of language ambiguity detection into Deselaers technique to provide the known and expected uses and benefits of Mesheryokov technique over detecting problematic statement technique of Deselaers. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Mesheryokov to Deselaers in order to determine the ambiguity in the sentences. (Refer to Mesheryokov paragraphs [0003].)
As per claim 20, The system of claim 15, Deselaers does not explicitly disclose the following which would have been obvious in view of Mesheryokov from similar field of endeavor “wherein to determine the plurality of types of information, the one or more devices are configured: determine a first type of information that identifies one or more items detected in the digital content and determine a second type of information that identifies a relationship between two or more words included in the digital content;”(Mesheryakov, ¶[0004] discloses analyze a sentence of a first text to determine syntactic relationships among generalized constituents of the sentence. ¶ [0036] discloses a rough syntactic analyzer show possible syntactic relationships in the sentence, which is expressed in creating a graph of generalized constituents. which is expressed in creating a graph of generalized constituents based on lexical-morphological analysis performed (by a lexical analyzer) on the lexical-morphological structure. The graph of generalized constituents is an acyclic graph in which the nodes are generalized (meaning that they store all the alternatives) lexical values for words in the sentence, and the edges are surface (syntactic) slots expressing various types of relationships between the combined lexical values. The graph of generalized constituents at the surface model level reflects all the possible relationships between words of the source sentence. ¶[0042] discloses the lexical-morphological structure of the sentence analyzed, including certain word groups, words in brackets, quotation marks, and similar items. )
“and wherein, to determine one or more unintended interpretations, the one or more devices are configured: determine one or more unintended interpretations based on the one or more items or the two or more words.”( Mesheryakov, ¶[0030] discloses the system checking text for ambiguous sentences, The user may be given the opportunity to look at an identified ambiguity and various ways of interpreting a sentence having the ambiguity. See ¶[0126], then ¶[0130] discloses ambiguity may be seen in the sentence: "THE SOIL SHALL BE COVERED BY FERTILIZER BEFORE IT FREEZES". Suppose we have three sentences in three different languages. One sentence is the source English sentence, which contains ambiguity. Two other sentences might be the translations into Russian and German respectively. If people or a machine translation system made the translation and the ambiguity in the source English sentence was not identified, the result is the formation of sentences that differ in meaning. The ambiguity in the source English sentence is that the pronoun "it" may relate either to the noun "soil" or to the noun "fertilizer." For this reason, the translations to the target languages, such as Russian or German, will differ depending on what word the pronoun "it" relates to, and the meaning of the translated sentence will differ as a result. Similar sentences may be understood differently by different translators, so the translations will differ.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Mesheryokov technique of language ambiguity detection into Deselaers technique to provide the known and expected uses and benefits of Mesheryokov technique over detecting problematic statement technique of Deselaers. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Mesheryokov to Deselaers in order to determine the ambiguity in the sentences. (Refer to Mesheryokov paragraphs [0003].)
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAGHAYEGH AZIMA whose telephone number is (571)272-1459. The examiner can normally be reached Monday-Friday, 9:30-6:30.
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, Vincent Rudolph can be reached at (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHAGHAYEGH AZIMA/Examiner, Art Unit 2671