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 .
Claim Rejections - 35 USC § 101
Claims 1-22 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.
Claim 1
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a computer-implemented method for storage of digital information data via at least one processing unit operatively coupled to at least one database that performs at least one step. Thus, the claim is a process, which is one of the statutory categories of invention under 35 U.S.C. § 101.
Step 2a Prong 1 This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Limitation “performing, via the processing unit, at least one syntactic and/or semantic search in the at least one database based upon the portion of the digital information data” The limitation recites performing a search operation that involves comparing input data against stored content to identify matches or similarities. This is classified as a Mental Process because it describes an evaluation or judgment step. Under 2019 PEG, mental processes include observations, evaluations, judgments, and opinions. The “syntactic” aspect involves exact matching (a form of evaluation), while the “semantic” aspect involves meaning-based comparison (a judgment step).
Limitation “wherein for selecting the at least one relevant meta-data string, the processing unit computes word and/or documents embeddings for named entities of the one or more documents of the at least one database” The limitation recites computing word and/or document embeddings - mapping words, phrases, or entire documents to vectors of real numbers. This is classified as a Mathematical Concept because it involves mathematical calculations that produce numerical representations from textual input. Under the 2019 PEG, the enumerated groupings of abstract ideas (mathematical concepts, mental processes, and certain methods of organizing human activity) are not mutually exclusive. A single limitation may fall within more than one grouping. For limitation ““wherein for selecting the at least one relevant meta-data string, the processing unit computes word and/or documents embeddings for named entities of the one or more documents of the at least one database”, while it is classified as a Mathematical Concept, it could also be argued to recite a Mental Process because the computation of embeddings involves cognitive-like operations (grouping similar concepts together in vector space). However, for Step 2A Prong One purposes, the limitation is identified as reciting a judicial exception — specifically a Mathematical Concept — and the analysis proceeds accordingly.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, step ‘performing…” falls within the mental process grouping of abstract ideas and steps wherein “selecting…” falls within the math concept grouping of abstract ideas. Both limitations are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2a Prong 2, This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exceptions into a practical application of those exceptions or whether the claim is "directed to" the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The following limitations are classified as additional element(s):
providing, at the processing unit, at least one portion of the digital information data
providing, via the processing unit, one or more meta-data strings in response to the at least one syntactic and/or semantic search
receiving, at the processing unit, at least one relevant meta-data string, wherein the at least one relevant meta-data string is selected from the one or more meta-data strings
storing, in any of the at least one database, the portion of digital information data and the at least one relevant meta-data string
wherein the at least one relevant meta-data string is usable for a future syntactic and/or semantic search
MPEP 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field
The additional elements describe specific steps that interact with the mathematical concept (computing embeddings) and mental process (performing search). However, when evaluated individually and in combination, these additional elements do not integrate the exceptions into a practical application. The “providing” of digital information data is merely an input step; the “providing” of meta-data strings in response to a search is a generic output step that does not improve technology; the “receiving” and “selecting” steps are conventional selection operations; the “storing” step is a well-known computer function; and the “usable for future search” limitation does not itself effect an improvement. The claim as a whole does not reflect an improvement to the functioning of a computer or any other technology or technical field.
MPEP 2106.05(b) Particular Machine
The processing unit is not tied to any particular machine or manufacture that improves its functioning; it merely serves as a conventional tool for performing the mental process (search) and mathematical concept (embedding computation). Similarly, the "database" is a generic storage medium. When evaluated individually and in combination, these elements do not impose meaningful limits on the judicial exceptions.
MPEP 2106.05(c) Particular Transformation
The “storing” step involves storing data and metadata, but this is not a particular transformation of an article to a different state or thing that meaningfully limits the abstract idea. No physical or tangible article undergoes a meaningful transformation as a result of the claim's operations. When evaluated individually and in combination with the other additional elements, this step does not provide an inventive concept that integrates the mathematical concept or mental process into a practical application.
MPEP 2106.05(e) Other Meaningful Limitations
The “providing,” “receiving,” and “selecting” steps are conventional data handling operations that do not meaningfully limit the abstract ideas. When evaluated individually and in combination, these elements do not add anything that meaningfully integrates the judicial exceptions into a practical application beyond what is well-understood, routine, and conventional.
MPEP 2106.05(g) Insignificant Extra-Solution Activity
The “providing” of digital information data is merely a user input step. The “storing” step is extra-solution- it merely preserves the results for later use. When evaluated individually and in combination, these elements do not integrate the judicial exceptions into a practical application because they represent well-understood, routine, conventional activities that do not add significantly more to the recited abstract idea.
MPEP 2106.05(h) Field of Use and Technological Environment
The additional elements are simply a field of use that attempts to limit the abstract idea to a particular technological environment. When evaluated individually and in combination with the other additional elements, this limitation does not provide an inventive concept that integrates the judicial exceptions into a practical application.
Step 2b, The additional elements, both individually and as an ordered combination, do not amount to significantly more than the judicial exception itself. The claim recites conventional computer operations (providing input, performing search, generating output, selecting from results, storing data) that are well-understood, routine, and conventional in the field of information retrieval. The mathematical concept of computing word/document embeddings is applied in a generic manner — it is not tied to any specific technical improvement or unconventional configuration. Similarly, the mental process of searching is performed using conventional search techniques without any inventive arrangement. The ordered combination does not provide an inventive concept because: (1) each additional element performs its function conventionally; (2) there is no meaningful interaction between the elements that produces a result beyond what would be expected from applying the abstract ideas in their ordinary context; and (3) the claim as a whole amounts to no more than implementing the abstract idea of "searching and storing metadata" using generic computer components. The claim does not improve technology, solve a technical problem with a technical solution, or provide any other meaningful limitation that would render it patent eligible under 35 U.S.C. § 101.
Claim 2 recites “wherein the relevant meta-data string is selected automatically by the processing unit and/or the relevant meta-data string is a user specified meta-data string.” This step simply observes meta-data strings and selects one of the meta-data strings as a relevant meta-data string. It is clearly mental observations or evaluations that are practically performed in the human mind. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 5 recites “wherein the method comprises at least one autocomplete step, wherein while providing the portion of digital information data at least one suggested vocabulary is provided from the database by the user interface depending on an input provided so far” Autocomplete feature simply observing the current word and suggesting a next word. It is clearly mental observations or evaluations that are practically performed in the human mind. Further, using computer resource as a tool to perform the metal process. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 6 recites “wherein the database comprises at least one document store, wherein the document store is configured for storing digital information data, wherein entries stored in the document store are indexed by using unique identifiers.” Storing data does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 7 recites “wherein the database comprises at least one knowledge base comprising a plurality of concepts, wherein the provided meta-data strings comprise information about at least one concept.” Provided data comprises is a post-solution activity. The claim is not patent eligible.
Claim 8 recites “wherein the at least one relevant meta-data string is provided by contextualizing the portion of digital information data, wherein the portion of digital information data is annotated with at least one concept of the knowledge base.” Annotating data does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 9 recites “wherein the portion of digital information data is stored and indexed using a unique identifier in the document store, wherein the relevant meta-data string is stored with the unique identifier in the knowledge base.” Storing data does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 10 recites “wherein the syntactic and/or semantic search comprises performing a document search query based on the portion of digital information data, wherein the portion of digital information data is compared syntactically and/or semantically to digital information data stored in the database, wherein a syntactic and/or semantic search index is provided by the processing unit.” Searching data in database does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 11 recites “wherein a search result ranking is provided by the processing unit, wherein search results are ranked in the search result ranking by similarity to the portion of digital information data” Ranking data simply organizes data in certain order. The claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 12 recites “ A computer-implemented method of retrieval of digital information data of at least one database via at least one processing unit operatively coupled to the database, wherein the digital information data has been stored by using the method of claim 1 for storage of digital information data, wherein the method comprises: providing, at the processing unit, at least one search query comprising one or more meta- data strings; and providing, via the processing unit, the digital information data from the database annotated with the one or more meta-data strings.” Searching data in database does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 13 is similar to claim 1. The claim is rejected based on the same reason.
Claim 14 recites “the multicore processor is configured for providing at least one search query comprising one or more meta-data strings, wherein the multicore processor is configured for providing the digital information data from the database annotated with the one or more meta-data strings.” Searching and receiving data from database does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 15 recites “wherein the information storage and retrieval system is configured for performing the method of claim 1” The claim is reject based on the same reasons explained in claim 1
Claim 16 recites ”wherein the database comprises at least one document store configured for storing at least documents and/or digital information data, wherein entries stored in the document store may be indexed by using unique identifiers (IDs).” Data storing does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 17 recites “wherein syntactic and/or semantic search comprises performing a document search query based on the portion of digital information data, wherein the document search query comprises determining a syntactic and/or semantic similarity between the portion of digital information data and entries of a document store.” Searching and receiving data from database does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 18 recites “wherein the database comprises at least one knowledge base comprising a plurality of concepts, wherein each of the concepts is linked to at least one entry of the at least one document store configured for storing at least documents and/or digital information data, and/or wherein the database comprises at least one knowledge base comprising a plurality of meta- data strings, wherein one or more concept(s) of the knowledge base are represented by a meta-data string, wherein one or more concepts are linked to at least one entry of at least one document store, wherein the meta-data string comprises information about connected entries of the at least one document store and connection to other concepts.” Knowledge-based recited in the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 19 recites “wherein a syntactic and/or semantic search index is provided by the processing unit, wherein the search syntactic and/or semantic search index includes a database comprising indexed documents, wherein a knowledge base comprising a plurality of meta-data strings comprises for each entry of a document store a unique identifier, wherein the processing unit determines and provides a corresponding meta-data string for entries of the syntactic and/or semantic search index, wherein the meta-data strings provided in response to the at least one syntactic and/or semantic search comprises information about at least one concept stored in a knowledge database.” Searching and receiving data from database does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 20 recites “wherein the provided portion of digital information data is evaluated using semantic information extraction and linked automatically to found concepts, wherein the selection of the relevant meta-data string comprises at least one named entity recognition.” Searching and receiving data from database does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 21 recites “wherein a user selects the relevant meta-data string, wherein a user interface displays suggested concepts, and allows the user to confirm and/or reject the suggested concepts” Selecting relevant meta-data strings does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 22 recites “wherein a user interface is configured for highlighting terms of the portion of digital information data that correspond to concepts of the knowledge base and/or for highlighting terms of search results corresponding to the portion of digital information data.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Claim(s) 1-2, 6-11, 13, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1)
Claim 1
Medelyan discloses a computer-implemented method for storage of digital information data via at least one processing unit ([0057], programable processor) operatively coupled to at least one database (fig. 6, repository 125, knowledge bases 690), the method comprising:
providing, at the processing unit ([0057], programable processor), at least one portion of the digital information data ([0016], line 3-4, “… receiving, or accessing, at 110 one or more documents 105…”);
performing, via the processing unit, at least one syntactic and/or semantic search in the at least one database based upon the portion of the digital information data ([0019], line 3-5, “… concepts may be obtained by matching text extracted from documents 105 against knowledge bases, such as Wikipedia, thesauruses, taxonomies… to perform text matching…” text matching [Wingdings font/0xF3] syntactic search, [0027], line 7-11, “… determine whether the ngram for the entity "apple" extracted from documents 105 corresponds to the meaning of related concepts extracted at 115, such as "apple" referring to the fruit, "Apple" referring to the company, and the like…” <examiner note: search based on meaning [Wingdings font/0xF3] semantic search>);
providing, via the processing unit, one or more meta-data strings in response to the at least one syntactic and/or semantic search ([0036], line 1-2, “… FIG. 4 depicts the ngram "apple" 405 mapped to the concept apple 410 the fruit and Apple 415 the company…” <examiner note: concept 410 has metadata string: Apple and concept 415 has metadata string: Apple>);
receiving, at the processing unit, at least one relevant meta-data string, wherein the at least one relevant meta-data string is selected from the one or more meta-data strings ([0037], line 3-5, “… the semantic relatedness between the ngram and each of the ambiguous/candidate concepts may be determined…” [0040], line26-27, “… the given document apple (the fruit) 410 may be selected…”); and
storing, in any of the at least one database, the portion of digital information data and the at least one relevant meta-data string ([0022], line 16-19, “… repository 125 may store these three objects as 3 ngrams "San Francisco," "library," and "books." The RDF 200 may thus provide a standard format for accessing and/or storing one or more ngrams 206 extracted from documents 105, one or more concepts 210 mapped to the ngrams 206…”),
wherein the at least one relevant meta-data string is usable for a future syntactic and/or semantic search [0022], line 16-19, “… repository 125 may store these three objects as 3 ngrams "San Francisco," "library," and "books." The RDF 200 may thus provide a standard format for accessing and/or storing one or more ngrams 206 extracted from documents 105, one or more concepts 210 mapped to the ngrams 206…”)
However, Medelyan does not explicitly disclose wherein for selecting the at least one relevant meta-data string, the processing unit computes word and/or documents embeddings for named entities of the documents.
Patra discloses wherein for selecting the at least one relevant meta-data string, the processing unit computes word and/or documents embeddings for named entities of the documents of the at least one database ([0021], “… FIG. 1… an entity linking system… generates a knowledge graph from a knowledge base (e.g., DBpedia)…” [0030], “… To create the knowledge graph, the graph building component 106 converts the RDF data, such that <subject> and <object> entries become vertices, and <relation> information becomes is a directed edge between subject and object vertices…” [0030], “… The subject denotes the entity or resource, and the relation denotes traits or aspects of the entity and expresses a relationship between the subject and the object…” [0036], “… At block 208, the embeddings component 108 learns embeddings or representations from the knowledge graph. A type of representation learning utilized herein is vertex-level representation or embedding learning…” [0039], “… the embeddings component 108 generates word vector embeddings using Word2vec, which is a group of related models that are used to produce word vector embeddings…” [0042], “… At block 302, the NER component 110 receives input text or document for entity linking analysis… At block 304, the NER component 110 extracts mentions from the received input text… uses a name tagger to extract entity mentions…” [0043] At block 306, the candidate finder component 112 identifies a set of one or more candidate entities for each extracted mention…” [0048], “… At block 308, the disambiguation component 114 evaluates the set of candidate vertices for each extracted mention to select the best candidate vertex for each mention and provide final linked entities for the given input document…” <examiner note: The best candidate vertex is considered as meta-data string the entity/concept that is selected for the entity that is mentioned in input document>
Medelyan discloses wikification tool, an automated subject indexing tool, or any text analytics service/application programming interface (API) configured to perform text matching. However, Medelyan does not explicitly disclose computing word and/or documents embedding for named entities of the document of the at least one database. Patra discloses entity linking system for selecting the at least one relevant meta-data string (i.e., best candidate vertex) based on computing word and/or documents embeddings for named entities/vertex of the documents of DBpedia database. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the unsupervised learning techniques as disclosed by Patra into Medelyan because the entity linking system is able to process short documents that contain only a relatively low number of mentions. An entity linking system should also be able to deal with evolving information, and to integrate updates in the knowledge base.
Claim 2
Claim 1 is included, Medelyan discloses wherein the relevant meta-data string is selected automatically by the processing unit and/or the relevant meta-data string is a user specified meta-data string ([0037], line 3-5, “… the semantic relatedness between the ngram and each of the ambiguous/candidate concepts may be determined…” [0040], line26-27, “… the given document apple (the fruit) 410 may be selected…” [0041], “… a threshold value may be used in conjunction with similarity scores to determine whether a concept is an exact match to the ngram, a close match to the ngram, or discarded as dissimilar to the ngram…” <examiner note: user et threshold to select relevant concept>)
Claim 6
Claim 1 is included, Medelyan discloses wherein the database comprises at least one document store, wherein the document store is configured for storing digital information data ([0017], libe 5, “… documents 105 may be stored…” [0022], line 16-19, “… repository 125 may store these three objects as 3 ngrams "San Francisco," "library," and "books."), wherein entries stored in the document store are indexed by using unique identifiers ([0023], “… may include an identifier (or locator) 202 for a document 105 from which the ngram was extracted, position information 208 for the ngram, mapping(s) 212 to one or more candidate concepts 210 extracted from knowledge bases at 115 (or annotated at 120), entity type information 203 for the ngram, a probability score 204 representative of how likely the ngram is of a particular entity type…”)
Claim 7
Claim 1 is included, Medelyan discloses wherein the database comprises at least one knowledge base comprising a plurality of concepts, wherein the provided meta-data strings comprise information about at least one concept ([0019], “… Concepts may be obtained by matching text extracted from documents 105 against knowledge bases, such as Wikipedia, thesauruses, taxonomies, and the like, containing concepts…”)
Claim 8
Claim 1 is included, Medelyan discloses wherein the at least one relevant meta-data string is provided by contextualizing the portion of digital information data ([0034], “… At 305, a set of labels are collected for the ngram extracted from the document. For example, the context of the ngram may be expressed as a set of labels representing concepts co-occurring in the document…”), wherein the portion of digital information data is annotated with at least one concept of the knowledge base ([0024], “… FIG. 1A, one or more of the concepts extracted at 115 may be annotated, at 120, with unique identifiers for those concepts when found in another knowledge base…”)
Claim 9
Claim 1 is included, Medelyan discloses wherein the portion of digital information data is stored and indexed using a unique identifier in the document store ([0022], “… each occurrence of a concept extracted at 110 from document 105 may be stored as an ngram 206 with associated metadata describing that ngram 206…” [0017], “… the extracted text may be used in an index of concepts contained in documents 105…”
Patra discloses wherein the relevant meta-data string is stored with the unique identifier in the knowledge base ([0030] In the RDF format, data is stored as triples in the form <subject><relation><object>. The subject denotes the entity or resource, and the relation denotes traits or aspects of the entity and expresses a relationship between the subject and the object. An illustrative RDF triple is <Sacramento> <CapitalOf <California>..”)
Claim 10
Claim 1 is included, Medelyan discloses wherein the syntactic and/or semantic search comprises performing a document search query based on the portion of digital information data, wherein the portion of digital information data is compared syntactically and/or semantically to digital information data stored in the database, wherein a syntactic and/or semantic search index is provided by the processing unit ([0019], line 3-5, “… concepts may be obtained by matching text extracted from documents 105 against knowledge bases, such as Wikipedia, thesauruses, taxonomies… to perform text matching…” text matching [Wingdings font/0xF3] syntactic search, [0027], line 7-11, “… determine whether the ngram for the entity "apple" extracted from documents 105 corresponds to the meaning of related concepts extracted at 115, such as "apple" referring to the fruit, "Apple" referring to the company, and the like…” <examiner note: search based on meaning [Wingdings font/0xF3] semantic search>)
Claim 11
Claim 10 is included, Medelyan discloses wherein a search result ranking is provided by the processing unit, wherein search results are ranked in the search result ranking by similarity to the portion of digital information data ([0036], line 1-2, “… FIG. 4 depicts the ngram "apple" 405 mapped to the concept apple 410 the fruit and Apple 415 the company…” [0037], line 3-5, “… the semantic relatedness between the ngram and each of the ambiguous/candidate concepts may be determined…” [0040], line26-27, “… the given document apple (the fruit) 410 may be selected…”)
Claim 13 is similar to claim 1. The claim is rejected based on the same reason.
Claim 15
Claim 13 is included, Medelyan and Patra disclose wherein the information storage and retrieval system is configured for performing the method of claim 1 (see claim 1)
Claim 16
Claim 1 is included, Medelyan discloses wherein the database comprises at least one document store configured for storing at least documents and/or digital information data, wherein entries stored in the document store may be indexed by using unique identifiers (IDs) ([0017], “… documents 105 may be stored… extract text from the documents 105 … to generate index, based on the extracted text, an index for documents 105…” [0024], “… concepts extracted at 115 may be annotated, at 120, with unique identifiers for the concepts when found in another knowledge base…”)
Claim 20
Claim 1 is included, Medelyan discloses wherein the provided portion of digital information data (concepts extracted at step 115) is evaluated using semantic information extraction (0019], line 1-2, “… At 115, once the documents 105 are converted into text concepts may be extracted at 115 from documents 105…” [0027], “… At 130, disambiguation may be performed to resolve ambiguities in the concepts extracted at 115… For example, a document 105 may contain the following sentence: "Apple is a fruit that grows in many western countries and is often used for making apple juice." In this example, disambiguation may determine whether the ngram for the entity "apple" extracted from documents 105 corresponds to the meaning of related concepts extracted at 115, such as "apple" referring to the fruit, "Apple" referring to the company, and the like. To determine whether the concepts truly share the same meaning and thus should be mapped to the same ngram, a disambiguator may perform at 130 disambiguation to determine which of the plurality of concepts are likely to be properly related to a given ngram extracted from documents 105…”) and linked automatically to found concepts ([0024], “… one or more of the concepts extracted at 115 may be annotated, at 120, with unique identifiers for those concepts when found in another knowledge base…”), wherein the selection of the relevant meta-data string comprises at least one named entity recognition ([0022], “… For example, a document may include a sentence with 11 words, such as the following: "San Francisco has a great public library with "thousands of books." In this example, the occurrences of three concepts "San Francisco," (a city) "library" (an institution)…”)
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Hurst (U.S. Pub 2012/0078945 A1)
Claim 5
Claim 1 is included, however, Medelyan does not explicitly disclose wherein the method comprises at least one autocomplete step, wherein while providing the portion of digital information data at least one suggested vocabulary is provided from the database by the user interface depending on an input provided so far.
Hurst discloses wherein the method comprises at least one autocomplete step, wherein while providing the portion of digital information data at least one suggested vocabulary is provided from the database by the user interface depending on an input provided so far ([0021], line 5-12, “... a document the author is creating is parsed to identify a unique term(s), and, one or more semantic concepts (e.g., URNs, URLs, etc.) are identified based upon the identified unique term(s). A list of one or more semantic concepts associated with the identified unique term(s) may be provided to the author, and the author can select a semantic concept, which may be inserted into the author created document (e.g., as an annotation)...”)
Medelyan discloses user at client device provides documents to the system and the documents are analyzed to determined concepts from knowledge database to associate with the document. However, Medelyan does not explicitly disclose concepts/vocabulary are provided while documents are provided. Hurst disclose concepts/vocabulary are annotated to the document while it is inputting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include auto suggests concepts to the text while it is inputting to allow user to understand the context and/or meaning of terms in the input text/document.
Claim(s) 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Bolla (U.S Pub 2019/0266158 A1)
Claim 12
Medelyan discloses a computer-implemented method of retrieval of digital information data of at least one database via at least one processing unit operatively coupled to the database, wherein the digital information data has been stored by using the method of claim 1 for storage of digital information data ([0017], documents 105 may be stored),
However, Medelyan does not explicitly disclose wherein the method comprises: providing, at the processing unit, at least one search query comprising one or more meta- data strings; and providing, via the processing unit, the digital information data from the database annotated with the one or more meta-data strings
Bolla discloses providing, at the processing unit, at least one search query comprising one or more meta- data strings (concepts) ([0051], “... receive the search query...” [0053], “... determine one or more concepts corresponding to the one or more query segments...”; and providing, via the processing unit, the digital information data from the database annotated with the one or more meta-data strings ([0055], “... retrieve the set of documents from the structured database based on the one or more concepts corresponding to the one or more query segments...”)
Medelyan discloses documents, identified concepts are stored; however, Medelyan does not explicitly disclose providing at least one search query comprising one or more meta- data strings; and providing the digital information data from the database annotated with the one or more meta-data strings. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bolla into Medelyan because “Beneficially, the system provides retrieved documents comprising information related to entered search query as well as synonyms of the entered search query. Consequently, the user is provided with a complete and all-inclusive set of documents related to the search query”
Claim 14 is similar to claim 12. The claim is rejected based on the same reason.
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Kovacs (U.S. Pub 2019/0325070)
Claim 17
Claim 1 is included, however, Medelyan does not explicitly disclose wherein syntactic and/or semantic search comprises performing a document search query based on the portion of digital information data, wherein the document search query comprises determining a syntactic and/or semantic similarity between the portion of digital information data and entries of a document store.
Kovacs discloses wherein syntactic and/or semantic search comprises performing a document search query based on the portion of digital information data ([0055], line 1-5, “… when any parts of the query term within the search window are selected, the selected text may be immediately used as a new query text for narrowing or refining the scope of search…” [0058], line 5-8, “… the search result data may identify at least one source document that satisfies a particular search query…”), wherein the document search comprises determining a syntactic and/or semantic similarity between the portion of digital information data and entries of a document store ([0042], “… The search type selection tool 250 may be configured to allow the user to select a specific type of search. For example, the type of search may be set to “Keyword,” “Associative,” or “Similarity” by clicking on the check-box of the desired type. The “Keyword” search may be used to run a conventional search using one or more keywords, wherein the resulting documents contain at least one the one or more keywords. The “Associative” and “Similarity” searches may be used to run different kinds of semantic searches typically using longer coherent texts as a basis of the search…” [0028], “…The search server system 110 may include at least one search engine 120 and a user interface system 122 according to the present disclosure. In some embodiments, the search engine 120 is a semantic search engine. When the search engine 120 is configured to perform semantic search, the search server system 110 may further include a document store 116 of text documents generated from the source documents of various types…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the query window management system as disclosed by Kovacs into Medelyan because current search engines have limitations on the size of search query text. Thus, it is often difficult or impossible to perform such kind of searches effectively. The query window management system disclosed by Kovacs has no limitations regarding the length of search query text and includes a user interface may enable a user to input a search query of any length into the semantic search engine.
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Katwala (U.S. Pub 2018/0046764 A1)
Claim 18
Claim 1 is included, Medelyan discloses wherein the database comprises at least one knowledge base comprising a plurality of concepts ([0019], line 4-5, “… knowledge bases, such as wikipedia… containing concepts),
However, Medelyan does not disclose wherein each of the concepts is linked to at least one entry of the at least one document store configured for storing at least documents and/or digital information data, and/or wherein the database comprises at least one knowledge base comprising a plurality of meta-data strings, wherein one or more concept(s) of the knowledge base are represented by a meta-data string, wherein one or more concepts are linked to at least one entry of at least one document store, wherein the meta-data string comprises information about connected entries of the at least document store and connection to other concepts.
Katwala discloses wherein each of the concepts is linked to at least one entry of the at least one document store configured for storing at least documents and/or digital information data ([0050], line 1-3, “… candidate concept instances are identified in each document that correspond to concepts in a taxonomy…” [0057], “… generating an index for each enriched document 108… Such an index may include references to linked instances/concepts or concept attributes, or an extracted list of entities/concepts…” <examiner note: concepts in the taxonomy (i.e., knowledgebase) is linked to enriched document in the index (i.e., entry in the document store)>), and/or wherein the database comprises at least one knowledge base (taxonomy) comprising a plurality of meta-data strings, wherein one or more concept(s) of the knowledge base are represented by a meta-data string ([0043], line 3-4, “… a semantic taxonomy… contains a plurality of concepts… example concept “breast cancer” 302a <examiner note: the text “breast cancer” is considered as a meta-data string for the concept “breast cancer”>), wherein one or more concepts are linked to at least one entry of at least one document store [0050], line 1-3, “… candidate concept instances are identified in each document that correspond to concepts in a taxonomy…” [0057], “… generating an index for each enriched document 108… Such an index may include references to linked instances/concepts or concept attributes, or an extracted list of entities/concepts…”), wherein the meta-data string comprises information about connected entries of the at least one document store ([0057], “… generating an index for each enriched document 108… Such an index may include references to linked instances/concepts or concept attributes, or an extracted list of entities/ concepts…”) and connection to other concepts ([0043], line 1012, “… concepts 302 may additionally be associated with concept relationships 306 that define how one concept is related to another concept…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the data enrichment process as disclosed by Katwala into Medelyan because the index is generated by the data enrichment process for each enriched document such an index may include references to linked instances/concepts or concept attributes, or an extracted list of entities/concepts.
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Lucas (U.S. Pub 2020/0176098 A1)
Claim 19
Claim 1 is included, Medelyan discloses wherein a syntactic and/or semantic search index is provided by the processing unit, wherein the search syntactic and/or semantic search index includes a database comprising indexed documents ([0017], “… documents 105 may be stored…” [0022], line 16-19, “… repository 125 may store these three objects as 3 ngrams "San Francisco," "library," and "books." [0017], “… generate, based on the extracted text, an index for documents 105…”), wherein a knowledge base comprising a plurality of meta-data strings ([0036], line 1-2, “… FIG. 4 depicts the ngram "apple" 405 mapped to the concept apple 410 the fruit and Apple 415 the company…” <examiner note: concept 410 has metadata string: Apple and concept 415 has metadata string: Apple>), wherein the meta-data strings provided in response to the at least one syntactic and/or semantic search comprises information about at least one concept stored in a knowledge database ([0036], line 1-2, “… FIG. 4 depicts the ngram "apple" 405 mapped to the concept apple 410 the fruit and Apple 415 the company…” [0019], line 3-5, “… concepts may be obtained by matching text extracted from documents 105 against knowledge bases, such as Wikipedia, thesauruses, taxonomies… to perform text matching…” <examiner note: concept 410 has metadata string: Apple and concept 415 has metadata string: Apple>)
However, Medelyan does not explicitly disclose wherein a knowledge base comprises for each entry of a document store a unique identifier, wherein the processing unit determines and provides the corresponding meta-data string for entries of the syntactic and/or semantic search index
Lucas discloses wherein a knowledge base (UMLS) comprises for each entry of a document store (entry [Wingdings font/0xF3] clinical document, a database of multiple document) a unique identifier (CUI, RXNORM) ([0096], “… upon receiving a record update or a request in the form of a clinical document, a database of multiple documents, or another form of patient record, the request may pass through a pre-processing subroutine, a parsing subroutine, a dictionary lookup subroutine, a normalization subroutine, a structuring subroutine for filtering and/or ranking, and a post-processing subroutine in order to generate and serve a response to a remainder of the system…” [0209], “… Text: The entirety of the text (i.e., “The patient was given Tylenol 50 mg at 10:35 am.”)...” [0214], “ [0214], “… UMLS_CUI: The CUI field (i.e., C0711228) of the UMLS entry corresponding to the medication. The UMLS is a list of medical concepts and the UMLS_CUI refers to the CUI field, which is UMLS' universal identifier…” [0215], “… UMLS_AUI: The AUI field (i.e., RXNORM #4459) is the dictionary-specific identifying code of the UMLS. Where the CUI is universal, and has the same entry across all included sources, the AUI for Tylenol will have different AUIs for each dictionary that it has an entry in…”), wherein the processing unit determines and provides the corresponding meta-data string for entries of the syntactic and/or semantic search index ([0210] … [0215])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the processing pipeline as disclosed by Lucas into Medelyan because instead of a data analyst with sufficient medical knowledge and access to the requisite databases to analyze and to map concepts in knowledge to concepts in the documents, the processing pipeline uses a combination of machine learning algorithms (MLA) and natural language processing (NLP) algorithms into this process may substantially improve the efficiency of the analysts or replace them altogether.
Claim(s) 21 is rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Ng Tari (U.S. Pub 2017/0329842 A1)
Claim 21
Claim 1 is included, however, Medelyan does not explicitly disclose wherein the user selects the relevant meta-data string, wherein the user interface displays suggested concepts, and allows the user to confirm and/or reject the suggested concepts.
Ng discloses wherein the user selects the relevant meta-data string, wherein the user interface displays suggested concepts, and allows the user to confirm and/or reject the suggested concepts ([0033], “… once an extracted concept 36 is disambiguated as 38… it is important to validate the disambiguated concept 38…” [0034], “… all extracted and disambiguated concepts 38 are presented to a user 52 on a web-based user interface, wherein text representing the concept 38 is highlighted. Alongside the highlighted text, the extracted and disambiguated concept 38 and its type are displayed. A typical user 52 is provided with two choices to indicate whether the extraction and disambiguation is correct (positive) or incorrect (negative)…” [0035], “… Further the user feedback module 16 validates the disambiguated concept with first manual input 62 received from a user 52, and generates either a retained concept 72 or a discarded concept 74…” <examiner note: the retained concept 72 is considered as the relevant meta-data string for the extracted concept 36>)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporating the system of text analysis disclosed by Ng into Medelyan because the problem of dealing with ambiguous terms while analyzing unstructured text is a common problem encountered by most entity recognition and entity linking algorithms. One approach to dealing with this challenge is modifying and improving the entity recognition and linking algorithms. However, modifying the original source code requires a significant amount of time as well as deep technical expertise and updating such systems would require compilation of labeled data to perform training and update the systems. Ng discloses a mechanism to effectively update the concept recognition and linking algorithms by means of user feedback. This mechanism eliminates the need to compile new training datasets to cover additional terms and decreases reliance on developers to update the system.
Claim(s) 22 is rejected under 35 U.S.C. 103 as being unpatentable over Medelyan (U. S. Pub 2014/0074860 A1), in view of Patra (U.S. Pub 2020/0342055 A1), as applied to claim 1, and further in view of Smathers (U.S. Patent 10990767 B1)
Claim 22
Claim 1 is included, however, Medelyan does not disclose wherein the user interface is configured for highlighting terms of the portion of digital information data that correspond to concepts of the knowledge base and/or for highlighting terms of the search results corresponding to the portion of digital information data.
Smathers discloses wherein the user interface is configured for highlighting terms of the portion of digital information data (the selected sentence) that correspond to concepts of the knowledge base (col 6, line 61-67, “… concept classification. Classifier 106… identify sentences that express known NLG concepts (concept sentence 108)…” col 13, line 37-40, “… at step 404, the classifier 404 labels the selected sentence with the concept corresponding to the matching hit… the selected sentence becomes associated with a concept…” fig. 8) and/or for highlighting terms of the search results corresponding to the portion of digital information data (fig. 8, “driver analysis”, “peak”, “change”, “concentration” are considered as search results. When one of them is selected/highlights, the corresponding sentence is also highlighted)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Smathers into Medelyan because it provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.
Response to Arguments
Section Claim Objections – pg. 7
The objection to claim 17 is withdrawn as necessitated by Amendment
Section Claim Rejections under 35 U.S.C 112(b) – pg. 7-8
The rejection to claims 1, 2, and 5-22 are withdrawn as necessitated by Amendment
Section Claim Rejections under 35 U.S.C 101 – pg. 9
Applicant argues that “… Applicants respectfully submit that pursuant to the subject matter eligibility analysis under the PEG, the claims are not directed to a judicial exception and are patent eligible for at least the following reasons. While it is not contested that the claims are respectively directed to a process, the Examiner asserts that claims 1-2 and 5-22 are directed to the judicial exception of an abstract idea. However, claims 1-2 and 5-22 do not recite concepts that explicitly fall into the abstract idea exception categories of (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. Therefore for at least this reason, the eligibility analysis should end at the first prong of Revised Step 2A…”
Applicant’s argument has been considered. Examiner respectfully disagrees because the claims recite judicial exceptions.
Limitation "performing... at least one syntactic and/or semantic search.” Semantic searching evaluates the meaning of the input data relative to the stored database entries. This process is inherently a function of human judgment, evaluation, and observation. The claim is therefore reciting an abstract idea-mental processes. Similarly, while “syntactic search” focuses on text matching, the overall combination of performing both types of searches represents a cognitive task that falls within the scope of mental processes.
Limitation “computes word and/or documents embeddings.” This limitation explicitly requires computing mathematical vectors (embeddings) for named entities. Under the 2019 PEG, the enumerated groupings of abstract ideas (mathematical concepts, mental processes, and certain methods of organizing human activity) are not mutually exclusive. A single limitation may fall within more than one grouping. Therefore, this limitation falls squarely within the definition of Mathematical Concepts as set forth in the PEG.
Therefore, the claims are directed to judicial exceptions at Step 2A Prong One.
Pg. 9-10
Applicant argues that “… Contrary to the Action's assertion that computing embeddings "can be performed by a person with the assist[ance] of paper and pencil" (Page 6-7 of the Action), the claimed method of claim 1 cannot reasonably be performed in the human mind. As the Specification explains, "word embeddings" refer to "a mapping of words from a vocabulary to vectors of real numbers," and "document embeddings" refer to "a mapping of one or multiple words and/or phrases from a document content to vectors of real numbers." ([0034 of the Specification) Computing such embeddings for named entities, i.e., mapping words to vectors of real numbers in a multi-dimensional vector space is a complex mathematical operation that cannot reasonably be performed in the human mind with or without the aid of pen and paper. Because the claimed embedding computation cannot practically be performed as a mental process, the claims are not directed to an abstract idea at Step 2A, Prong One, for this additional reason…”
The Applicant argues that because computing embeddings is a complex mathematical operation, it cannot be performed by a person with pen and paper. Therefore, the limitation “computing embeddings” does not qualify as a mental process under Step 2A Prong One.
Under the 2019 PEG, the enumerated groupings of abstract ideas (mathematical concepts, mental processes, and certain methods of organizing human activity) are not mutually exclusive. A single limitation may fall within more than one grouping.
The limitation “computing embeddings” explicitly recites (i.e., set forth or described) a process of computing mappings from words and documents to vectors of real numbers. Regardless of whether the computation is feasible for a human mind to perform manually, the claim limitation still recites a mathematical concept because it mathematically describes computing and mapping words to real numbers.
Pg. 10
Applicant argues that “… Even assuming arguendo that it could be determined that the claims somehow do explicitly recite a judicial exception, each of the claims integrates such recitation into a practical application and, therefore, should be deemed to be not "directed to" the patent- ineligible judicial exception in accordance with the second prong of the Revised Step 2A. Specifically, the practical application of the claims lies in creating a structured knowledge base that annotates a portion of digital information data with a relevant meta-data string computed using word and/or document embeddings, thereby enabling improved future syntactic and/or semantic retrieval of that digital information data, as described in the Specification. See [0008], [0009] and [0042] of the Specification. The techniques described in the Specification enable enhanced information retrieval and knowledge management by structuring otherwise unstructured digital information data with computed embeddings so that it can be more effectively retrieved and reused, rather than merely searching for and storing the data as alleged in the Action. See [0056] of the Specification The claimed combination of providing digital information data, performing an embedding- based syntactic and/or semantic search, receiving a relevant meta-data string selected using computed word and/or document embeddings, and storing the digital information data together with that meta-data string for future retrieval represents a specific technological solution to the problem of company-internal information retrieval described in the Specification and is not a mere automation of a mental process.
In view of the above, claim 1 is not directed to an abstract idea. Therefore, Applicant respectfully submits that independent claim 1 is directed to patent eligible subject matter under the second prong of Step 2A. Dependent claims 2, 5-12, 14-22 are also directed to patent eligible subject matter at least by virtue of their respective dependencies from claim 1. Similar language is also included in independent claim 13…”
The Applicant points to paragraphs [0008], [0009], [0042], and [0056] to show that the claims are a “specific technological solution.” Simply describing a useful process in the specification does not make the claim eligible if the claim does not provide a specific, non-generic limitation that transforms the abstract idea into a practical application.
The Applicant argues that the technical improvement comes from “creating a structured knowledge base that annotates... data with a relevant meta-data string computed using word and/or document embeddings.”
The combination of steps: providing data, performing search (semantic/syntactic), computing embeddings, receiving metadata, and storing results - is still fundamentally a method for organizing information based on abstract concepts like perform semantic/syntactic search or computing embeddings. The mere storage of structured data and the use of embeddings are considered standard methods within the field of computer science for structuring information. Adding extra steps does not change the fact that the core concept is just a mental process or math idea.
Because the claims still rely on fundamental concepts like semantic/syntactic search and embedding computation—which are classified as mental processes or mathematical concepts respectively—and because the additional elements (like providing data and storing results) describe standard computer data handling procedures, the claim is still directed to an abstract idea.
Pg. 11
Applicant argues that “… Even assuming arguendo that it could be determined that the claims somehow are not integrated into a practical application under the second prong of Step 2A, the claims add significantly more than the judicial exception under Step 2B. Specifically, the ordered combination of providing digital information data, performing a syntactic and/or semantic search, computing word and/or document embeddings for named entities to select a relevant meta-data string, and storing the digital information data together with that meta- data string for future retrieval is not well-understood, routine, or conventional activity. Rather, this ordered combination provides an inventive concept that improves the technical field of information retrieval and knowledge management by enabling structured, embedding-based annotation and retrieval of digital information data that was not previously achievable using conventional text-matching or manual tagging approaches, as described in the Specification…”
The Applicant argues that the ordered combination of steps recited in the claims- providing data, performing searches, computing embeddings for named entities, selecting metadata, and storing the results-is not well-understood, routine, or conventional activity.
The claims merely recite an ordered combination of steps, each of which represents routine technological activity:
Searching and Retrieval: Performing syntactic or semantic searches are fundamental, established functions in database technology.
Computational Steps (Embeddings): Computing word and document embeddings for named entities is a well-established mathematical technique used across the entire field of NLP.
Data Storage: Storing both the original data portion and the resulting metadata string is a standard storage practice required for database system.
The claims do not introduce an inventive concept. Instead, the claims combine several known building blocks (searching, vector computation, data storage) in a manner that is common within the current state of the art. The combination merely describes a predictable and expected technological workflow for modern digital systems.
Accordingly, the claimed process remains an application of conventional technology, lacking the necessary non-obviousness required to overcome the judicial exceptions.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
U.S. Pub 2021/0303638 – Zhong discloses a system for processing user-generated input. During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.
U.S. Pub 2021/0248323 – Maheshwari discloses techniques are described for intelligently identifying concept labels for a set of multiple documents where the identified concept labels are representative of and semantically relevant to the information contained by the set of documents. The technique includes extracting semantic units (e.g., paragraphs) from the set of documents and determining concept labels applicable to the semantic units based on relevance scores computed for the concept labels. The technique includes determining an initial set of concept labels for the set of documents based on the applicable concept labels. The technique further includes obtaining a reference hierarchy associated with the reference set of concept labels and determining a final set of concept labels for the set of documents using a reference hierarchy, the initial set of concept labels, and the relevance scores. The technique includes outputting information identifying the final set of concept labels for the set of documents.
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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/ Primary Examiner, Art Unit 2154
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