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 .
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 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.
Claim Rejections - 35 USC § 101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-12, and 14-19 are rejected under 35 USC 101 as directing toward non-statutory subject matter.
Claim 1 recites a method (i.e., a process). Claim 8 recites a computer program product (i.e., a manufacture). Claim 15 recites a system (i.e., a machine).Reply to Decision on Appeal of June 8, 2021
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application. Alice Corp. Pty. Ltd. v. CLS Bank Int’l., 134 S. Ct. 2347, 2355 (2014).
Step One (Step 2A) is a two prong test that requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept. See MPEP 2106.04.
Step 2A Prong (1) requires the determination of the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04(a).
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a).
If the claim recites an enumerated patent ineligible concept, then Prong (2) of Step One (Step 2A) requires the determination of whether the claim integrates the patent ineligible concept into a practical application. Individually and in combination, identifying whether there are any additional elements recited in the claim beyond the judicial exceptions and evaluating those additional elements to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. See MPEP 2106.04(d).
Under Step 2B, if the claim does not integrate the ineligible concept into a practical application and therefore directed to a judicial exception, evaluate whether the claim provides an inventive concept by determining whether there are additional elements, individually and in ordered combination, amount to significantly more than the exception itself. See MPEP 2106.04.
Step 2A Prong (1)
The “directed to” inquiry does not ask whether the claims involve a patent ineligible concept but, considered in light of the specification, whether the claim as a whole is directed to excluded subject matter or directed to an improvement to computer functionality. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016).
Therefore, Prong (1) of Step 2A requires identifying specific limitations in the claims that recites (“describes” or “set forth”) an abstract idea and determine whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04 (“Thus, it is sufficient for this analysis for the examiner to identify that the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) aligns with at least one judicial exception”).
Under Prong (1), Claim 1 recites a computer-implemented method, comprising:
(1) determining a domain and category information from metadata associated with a first webpage;
(2) determining component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage;
(3) constructing context information for the text elements;
(4) calculating, based on the grouping relationships and the context information, word vectors;
(5) extracting, based on the word vectors and the context information, text feature types of the text elements; and
(6) using the extracted text feature types to determine a re-ordering of a translation of the text elements.
Claim 8 recites a computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and/or executable by a computer to cause the computer to:
(1) determine a domain and category information from metadata associated with a first webpage;
(2) determine component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage;
(3) construct context information for the text elements;
(4) calculate, based on the grouping relationships and the context information, word vectors;
(5) extract, based on the word vectors and the context information, text feature types of the text elements; and
(6) use the extracted text feature types to determine a re-ordering of a translation of the text elements.
Claim 15 recites a system, comprising:
a processor; and
logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
(1) determine a domain and category information from metadata associated with a first webpage;
(2) determine component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage;
(3) construct context information for the text elements;
(4) calculate, based on the grouping relationships and the context information, word vectors;
(5) extract, based on the word vectors and the context information, text feature types of the text elements; and
(6) use the extracted text feature types to determine a re-ordering of a translation of the text elements.
With respect to (1), individually and considered in light of the specification US 2024/0427834 A1 at ¶49: “Operation 202 includes determining a domain and category information from metadata associated with a first webpage” and ¶50: “In some approaches, the domain and category information are determined by analyzing a predetermined target that is determined from the metadata”.
With respect to (2), individually and considered in light of the specification US 2024/0427834 A1 at ¶52: “The component types, hierarchies and grouping relationships are, in some approaches, determined from the metadata associated with the first webpage. Furthermore, the grouping relationships and context information may, in some approaches, be determined using a predetermined word embedding model. In yet some other approaches, the natural language processing (NLP) may be applied to analyze article structure, which the component types, hierarchies and/or grouping relationships may be based on”.
With respect to (4), individually and considered in light of the specification US 2024/0427834 A1 at ¶60: “Operation 208 includes calculating word vectors. In some preferred approaches, the word vectors are calculated based on the grouping relationships of the text elements and the constructed context information. In some approaches, calculation of the word vectors includes performing a predetermined process”.
With respect to (5), individually and considered in light of the specification US 2024/0427834 A1 at ¶ 61: “Operation 210 includes extracting, based on the text element word vectors and the context information, text feature types of the text elements. In some approaches, the text feature types may include, e.g., whether a word is a verb, whether a word is a noun, whether a word is an adjective, etc. In some preferred approaches, the extraction of the text feature types of the text elements is performed using a predetermined process. In one or more of such approaches, the predetermined process includes inputting some strings in the same group with vector, domain, category, type and other information, into a predetermined text feature type extractor model. In some approaches, the predetermined text feature type extractor model may be of a type that would become apparent to one of ordinary skill in the art after reading the descriptions herein. The predetermined process may additionally and/or alternatively include calculating each string text feature candidate types, and choosing a group text feature type based on group domain, category, type and other information”.
The Court of Appeals for the Federal Circuit (“CAFC”) held that analyzing information by steps people go through in their minds, mathematical algorithms as essentially mental processes within the abstract idea category. Electric Power Grp., 830 F.3d at 1353 (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”). Accordingly, steps (1)-(2) and (4)-(5) are essentially mental processes.
With respect to (3), individually and considered in light of the specification US 2024/0427834 A1 at ¶59: “In some preferred approaches, a predetermined text element context information constructor may be caused to collect all the context information, e.g., business domain, category, component type, hierarchy, group, etc., from a DOM tree elements analyzation module and then build the translation data structure based on the collected context information”.
Step (3) of collecting information, limited to a particular format (i.e., context information), corresponds to a mental process within the realm of abstract ideas. Electric Power Grp., 830 F.3d at 1353 (“we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract idea”). See also MPEP 2106.04(a)(2)IIIA (“a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind”).
With respect to (6), individually and considered in light of the specification US 2024/0427834 A1 at ¶61: “Operation 212 includes translating the text elements from the first language to the second language. In some approaches, a known type of text translation may be performed on the text elements. Once translated, the translation of the text elements is preferably reordered, e.g., see operation 214. In some approaches, the text elements are preferably reordered based on a context determined in one or more of the operations described above. For example, in one preferred approach, the extracted text feature types are used to determine a re-ordering of a translation of the text elements. In some approaches, this context and text elements may be applied to a predetermined plurality of linguistic rules for the second language to determine one or more of the re-ordering operations to perform”.
One known type of translation of text elements from a first language to a second language is a mental step of translating text from the first language into the second language through steps people go through in their minds. See MPEP 2106.04(a)(2)IIIA (“a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind”).
In ordered combination, steps (1)-(6) amounted to analyzing webpage metadata, analyzing webpage article structure, collecting context information, analyzing / calculating word vectors, and analyzing / calculating webpage text features types to mentally performing translation of webpage text from a first language into a second language that are essentially mental processes.
Thus, claims 1, 8, and 15 described patent ineligible subject matter enumerated under category (a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations, (b) Certain methods of organizing human activity –managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions), and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A Prong (2).
Under Prong (2) of Step 2A, the goal is to determine whether the claim is directed to the recited exception by evaluating whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. See MPEP 2106.04II(A).
In particular, evaluating integration into a practical application requires identifying whether there are any additional elements recited in the claim beyond the judicial exception and evaluating those additional elements, individually and in combination, to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit (“CAFC”). See MPEP 2106.04(d).
According to the Supreme Court, a patent may issue for the means or method of producing a certain result, or effect, and not for the result or effect produced. Diamond v. Diehr, 450 U.S. 175, 182 n. 7 (1981). Therefore, the focus is on whether the claim “focus on a specific means or method that improves the relevant technology or are instead directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery”. McRO, Inc. v. Bandai Namco Games America, Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016).
In particular, the Supreme Court and the CAFC distinguished between computer-functionality improvements from the uses of existing computers as tools in aid of processes focused on abstract ideas. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1354 (Fed. Cir. 2016) (“…we relied on the distinction made in Alice between, on one hand, computer-functionality improvement and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas”…”).
In an exemplary patent eligible automation claim, in McRO, the CAFC noted that prior art method of generating morph weight set with values between “0” and “1” for computer animation of facial expressions are manually determined. McRO, 837 F.3d at 1304-5. The claimed improvement in McRO allows computers to produce “accurate and realistic lip synchronization and facial expressions in animated characters” that previously could only be produced by human animators through the automated use of rules, rather than artists, to set the morph weights and transitions between phonemes. Id. at 1313.
Specifically, the claims were directed to the incorporation of claimed rules, not the use of the computer that improved existing technological process by allowing automation of further tasks that goes beyond merely organizing existing information into a new form. Id. at 1314-15.
In particular, the claimed process used a combined order of specific rules that renders information into a specific format that is then used and applied to create a sequence of synchronized, animated characters that prevent pre-emption of all processes for achieving automated lip-synchronization of 3-D characters. Id. at 1315. Therefore, the CAFC held that the ordered combination of claimed steps, using unconventional rules that relate sub-sequences of phonemes, timing, and morph weight sets is patent eligible. Id. at 1302-3.
See also MPEP 2106.04(d)I (“an improvement in the functioning of a computer or an improvement to other technology or technical field, as discussed in MPEP 2106.04(d)(1) and 2106.05(a)”).
On the other hand, in a case where selecting information for collection, analysis, and display by content or source that did nothing significant to differentiate a process from ordinary mental processes. Electric Power Grp., 830 F.3d at 1355. There, claims specified what information in the power-grid field it is desirable to gather, analyze, and display in “real time” but they do not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology. Id. at 1356.
In another example, in Intellectual Ventures I, the CAFC held that tailoring content as a function of the user’s personal characteristics is a fundamental practice long prevalent in our system and therefore an abstract idea. Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1369-70 (Fed. Cir. 2015).
The CAFC determined that while the claims recited interactive interface / web page manager which tailor webpage to specific individual based on profile, the interactive interface simply describes a generic web server with attendant software tasked with providing web pages to and communicating with user’s computer that amounts to “apply it on a computer”. Id. at 1370-71. That is, requiring the use of a software brain tasked with tailoring information and providing it to the user provides no additional limitation beyond applying an abstract idea, restricted to the internet, on a generic computer. Id. at 1371.
Further, the fact that web site returns pre-designed ad more quickly than a newspaper could send the user is not an inventive concept because merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea. Id. at 1370.
Finally, the CAFC further determined that a database, a user profile keyed to a user identity, and a communication medium are all generic computer elements such that instructing one to apply an abstract idea and reciting no more than generic computer elements performing generic computer tasks that does not make an abstract idea patent eligible. Id. at 1368.
As an ordered combination, steps (1)-(6) of claims 1, 8, and 15 of the instant application amounted to analyzing first webpage metadata, analyzing first webpage article structure, collecting context information of the first webpage, analyzing / calculating word vectors for the first webpage, and analyzing / calculating webpage text features types of the first webpage to mentally performing translation of first webpage text from a first language into a second language that are essentially mental processes; i.e., translating / tailoring the text elements of the first webpage into a different language.
Therefore, steps (1)-(6) of the instant application required no more than entirely conventional and generic technologies employed for selecting textual elements for collection, analysis / translation, and display by content or source in Electric Power Grp. Therefore, claims 1, 8, and 15 no more differentiate a process from ordinary mental processes as the claims in Electric Power Grp.
Further, unlike the patent eligible automation claims in McRO that set forth a specific means of automating animation (i.e., using a combined order of specific rules that renders information into a specific format that is then used and applied to create a sequence of synchronized, animated characters to improve a computer process through the automated use of rules), steps (1)-(6) of the instant application provided no particular rules or means of automating translation of the text elements on the first webpage to a different language.
The mere requirement that the method of claim 1 should be implemented by a computer, or requiring processor and computer readable storage medium / memory in claims 8 and 15 is akin to the generic web server with attendant software tasked with providing tailored webpage to specific individuals in Intellectual Ventures I, which amounted to “apply it on a computer”
The CAFC determined that while the claims recited interactive interface / web page manager which tailor webpage to specific individual based on profile, the interactive interface simply describes a generic web server with attendant software tasked with providing web pages to and communicating with user’s computer that amounts to “apply it on a computer”. In other words, applying an abstract idea (mentally performing a translation of first webpage or tailoring the first webpage in another language) and requiring no more than generic computer elements performing generic computer tasks does not make an abstract idea patent eligible because translating or tailoring the first webpage into another language did not go beyond merely organizing existing information (webpage text in a first language) into a new form (webpage text in a second language).
Therefore, as an ordered combination of computer components, claims 1, 8, and 15 do not integral abstract mathematical calculations and mental translation processes into a practical application and the claims are instead directed toward patent ineligible mentally translating / tailoring the text elements of the first webpage into a different language.
Step 2B Inventive Concept.
The Guideline stated that if the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B where it may still be eligible if it amounts to an “inventive concept”. See MPEP 2106.04IIA and MPEP 2106.05.
Further, an inventive concept can be found in the non-conventional and non-generic arrangement of known conventional pieces. BASCOM Global Internet Servs. v. AT&T Mobility, 827, F3d 1341, 1350 (Fed. Cir. 2016).
In BASCOM, the CAFC held that filtering content is an abstract idea because it is a longstanding, well-known method of organizing human behavior similar to concepts previously found to be abstract. BASCOM, 827 F.3d at 1348. However, the CAFC determined that the claims did not merely recite filtering content along with the requirement to perform it on the internet or on a set of generic computer components, nor did the claims preempt all ways of filtering content on the internet. Id. at 1350.
Rather, the inventive concept described and claimed was the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user that gives the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on an internet service provider “ISP” server. Id. By taking a prior art filter solution (one size fits all filter at internet service provider “ISP” server) and making it more dynamic and efficient (providing individualized filtering at the ISP server), the claimed invention improves the performance of the computer system itself. Id. at 1351.
On the other hand, implementation via computers does not offer a meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a “communications controller” and “data storage unit” capable of performing the basic calculation, storage, and transmission functions required by the method claims”). Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1370-71 (Fed. Cir. 2015) (“Steps that do nothing more than spell out what it means to “apply it on a computer” cannot confer patent-eligibility).
Similarly, limiting an abstract idea to one field of use do not convert otherwise ineligible concept into an inventive concept. Intellectual Ventures I L.L.C. v. Erie Indem. Co., 850 F.3d 1315, 1328 (Fed. Cir. 2017). Neither does adding computer functionality to increase the speed or efficiency of the process confer patent eligibility on an otherwise abstract idea. Intellectual Ventures I, 792 F.3d at 1367 (citing Bancorp Servs., LLC v. Sun Life Insurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”)).
In the instant application, the method of claims 1, 8, and 15 focused on steps (1)-(6) of claims 1, 8, and 15 of the instant application amounted to analyzing first webpage metadata, analyzing first webpage article structure, collecting context information of the first webpage, analyzing / calculating word vectors for the first webpage, and analyzing / calculating webpage text features types of the first webpage to mentally performing translation of first webpage text from a first language into a second language that are essentially mental processes; i.e., translating / tailoring the text elements of the first webpage into a different language.
In other words, unlike BASCOM that described an unconventional combination to provide both the benefits of a filter on a conventional local computer and the benefits of a filter on the conventional ISP server, the claims did not set forth any combination of components to improve a specifically asserted technology or implement a technological process.
That is, the mathematical calculations and analysis of steps (1)-(6) requiring a generic computer with processor and computer readable medium / memory required no more than generic computer components because nearly every computer will include a “communications controller” and “data storage unit” capable of performing the basic calculation and storage. Therefore, claims 1, 8, and 15 requiring computer implementations does not offer a meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment.
Therefore, claims 1, 8, and 15 do not supply an inventive concept.
Other dependent claims failed to integrate the abstract idea into a practical application or provide an inventive concept.
In particular, dependent claims 2, 9, and 16 required training a predetermined translation model to perform context-based re-ordering of text elements translated from a second website. However, the claims do not describe a particularly asserted structure of the predetermined translation model or a particular means or method or rules the predetermined translation model apply to perform the context-based re-ordering.
As stated in McRO, the claimed improvements were directed to the incorporation of claimed rules, not the use of the computer that improved existing technological process by allowing automation of further tasks that goes beyond merely organizing existing information into a new form. McRO, 837 F.3d at 1314-15.
Lacking a specifically asserted improvement or application of the model to the translation process, claims 2, 9, and 16 do not integrate the abstract idea of claims 1, 8, and 15 into a practical application or provide an inventive concept.
Dependent claims 3-5, 7, 10-12, and 17-19 further limit the analysis steps (1)-(3) and therefore are essentially mental steps.
For the above reasons, Claims 1-5, 7-12, and 14-19 are patent ineligible.
As for claims 6, 13, and 20 requiring wherein using the extracted text feature types to determine a re-ordering of translations of the text elements includes:
inputting strings of text elements of a same one of the grouping relationships into a predetermined translation engine, wherein the strings are input with: information about the word vectors, an indication of text feature types, and
associated portions of the context information;
obtaining outputs of the translation engine, wherein the outputs include target vectors for the strings of text elements;
obtaining target translations for the strings of text elements; and
re-ordering the strings of text elements according to the target translations.
Therefore, claims 6, 13, and 20 specifically asserted a translation engine that distinguished the ordered combination of steps (1)-(6) in claims 1, 8, and 15 from a purely mental translation process using steps that people go through in their minds. Specifically, the claims 6, 13, and 20 recite what particular information are required by the specifically asserted translation engine, the particular means or method of translation (associating portions of the context information when translating), and re-ordering (i.e., improving) the translation of the strings of text elements.
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-12, and 14-19 are rejected under 35 USC 103(a) as being unpatentable over Yuan et al. (CN 106570171 B, see attached IP.com translation) in view of Niu et al. (US 2009/0182547 A1) and Chen et al. (CN 120012787 A, see attached IP.com translation).
Regarding Claims 1, 8, and 15, Yuan discloses a system (Fig. 2, technology information processing system), comprising:
a processor (p. 13, “These computer program instructions may be provided to a processor of a general purpose computer”); and
logic integrated with the processor, executable by the processor, or integrated with and executable by the processor (p. 13, “such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks”), the logic being configured to:
determine metadata associated with a first webpage (p. 10, “After the target website data is automatically captured, the webpage content can be analyzed by adopting an Xpath template”);
determine component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage (p. 10, “the step of acquiring website data in step S1 includes: S11, capturing data of the target website through the web crawler to obtain first data…Xpath is a standard for W3C and is an expression language whose return values may be nodes, node collections, atomic values, and a mixture of node and atomic values”; atomic values – component types, nodes – hierarchies, node collections – grouping relationships);
construct context information for the text elements (p. 10, “s21, performing word segmentation and labeling on the sentences in the website data obtained in the step S1 to obtain source language phrases, wherein the source language phrases after word segmentation and labeling are more suitable for the translation process”);
extract, based on the text elements / words and the context information, text feature types of the text elements (p. 10, “s21, performing word segmentation and labeling on the sentences in the website data obtained in the step S1 to obtain source language phrases, wherein the source language phrases after word segmentation and labeling are more suitable for the translation process”); and
use the extracted text feature types to determine a re-ordering of a translation of the text elements (p. 10, “the step S2 of translating the website data in chinese/english by a decoding algorithm according to the chinese-english bilingual parallel corpus includes…And S22… finding the best translation result of the source language phrase by using a translation model according to a probability estimation method”; per p. 9, “The method comprises the steps of corpus cleaning, Chinese word segmentation, sentence and word alignment, language model and translation model learning, decoding, reordering and the like on bilingual resources”).
Yuan does not teach determine a domain and category information from the metadata associated with the first webpage.
Niu discloses cross-language translation (Abstract) determining a domain and category information from the metadata associated with the first webpage (¶¶69-70 and Fig. 5, xpath of a DOM tree node is defined as the string concatenating the tag’s HTML tag (e.g., TITLE) and the tags of all its parents (e.g., HEAD and HTML); e.g., Fig. 1 shows title or domain and category of a first webpage being (X Men) (20th Fox, USA)).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to determine a domain and category information from the metadata associated with the first webpage in order to analyze webpage content by adopting an Xpath template (Yuan, p. 10, “After the target website data is automatically captured, the webpage content can be analyzed by adopting an Xpath template”; Niu, ¶70, “The xpath of a DOM tree node is defined as the string concatenating the tag's HTML tag (e.g., TITLE) and the tags of all its parents (e.g., HEAD and HTML)”).
The combination of Yuan-Niu does not teach calculate, based on the grouping relationships and the context information, word vectors; extract, based on the word vectors, text feature types of the text elements.
Chen discloses non-autoregressive machine translation encoder-decoder (Abstract) for translating input sentence x by calculating word vectors based on grouping relationships and context (p. 8, “The length prediction module is used for determining the length of the translation in advance because the non-autoregressive machine translation completes the generation of all translations at the same time” and “First, for a sentence x to be translated, the encoder maps it into the source vector space and predicts the length L of the translated sentence. Note that at this point the encoder no longer performs the mask prediction process. The target input is then initialized at the target using a < MASK > sequence of length L” and in view of Chen, p. 5, “The semantic unit masking method based on the mutual information masks complete semantic units (words or phrases), so that the model is helped to learn the complete semantic information”; i.e., encoder maps input sentence (which are semantic units with grouping / semantic relationships) into source vector space / word vectors and predicts length L (i.e., context) of the translated sentence based on length of the input sentence in source language).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement the non-autoregressive machine translation encoder-decoder of Chen as the translation decoding algorithm of Yuan (p. 10, “Preferably, the step S2 of translating the website data in chinese/english by a decoding algorithm…”) to calculate word vectors based on grouping relationships and context to extract text feature types of text elements to determine the re-reodering of the translation thereof (Yuan, p. 9, “The method comprises the steps of corpus cleaning, Chinese word segmentation, sentence and word alignment, language model and translation model learning, decoding, reordering and the like on bilingual resources”; compare Chen, p. 8, “First, for a sentence x to be translated, the encoder maps it into the source vector space and predicts the length L of the translated sentence”; i.e., map aligned sentence and words (grouping relationships) into source vector space / word vectors and predict sentence length L as context for the word vectors) in order to help the decoding algorithm / translation model to learn the complete semantic information (Chen, p. 5).
Here, the established function of Yuan extracts text feature types of text elements based on (1) word segmentations and (2) sentence labeling that are more suitable for decoding translation process (Yuan, p. 10, s21). Chen provides such decoding translation process where words / word segments in sentences are mapped to word vectors and context of sentence are labelled / classified with a predicted length such that translation is performed based on complete semantic units (Chen, p. 5; i.e., using complete phrases or words as complete semantic unit information to predict missing contents by means of proximity complete semantic unit information as context).
Further regarding claim 8, Yuan discloses a computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and/or executable by a computer to cause the computer to perform the method of claim 1 and the functions of claim 15.
Regarding Claims 2, 9, and 16, Yuan discloses train a predetermined translation model based on the context information to perform a context based re-ordering of translated text elements (Yuan, p. 9, “S2…The method comprises the steps of corpus cleaning, Chinese word segmentation, sentence and word alignment, language model and translation model learning, decoding, reordering and the like on bilingual resources, so that a Chinese and English scientific and technical information translation engine is constructed”; in particular, p. 10, “S23, organizing the translated phrases by using a language model constructed by using a monolingual speech material library of the target language to generate sentences conforming to grammar rules”); and cause the trained translation model to perform a context based re-ordering of text elements translated from a second website (Yuan, p. 11, “According to the semantic-based scientific and technological information processing method, the classifier is obtained by finding out the classification rule of the scientific and technological information from the text training set through applying the classifier, and the classifier can automatically predict the class attribute of the information when new unknown information is obtained in the future”).
Regarding Claims 3, 10, and 17, Yuan as modified by Niu discloses wherein the domain and category information are determined by analyzing a predetermined target selected from the group consisting of: a uniform resource locator (URL), head information (Niu, ¶¶69-70 and Fig. 5, xpath of a DOM tree node is defined as the string concatenating the tag’s HTML tag (e.g., TITLE) and the tags of all its parents (e.g., HEAD and HTML); e.g., Fig. 1 shows title or domain and category of a first webpage being (X Men) (20th Fox, USA)), and a navigator (Niu, ¶68, extract navigation blocks).
Regarding Claims 4, 11, and 18, Yuan as modified by Niu discloses wherein the component types, hierarchies and grouping relationships are determined from the metadata associated with the first webpage (Yuan, p. 10, “the step of acquiring website data in step S1 includes: S11, capturing data of the target website through the web crawler to obtain first data…Xpath is a standard for W3C and is an expression language whose return values may be nodes, node collections, atomic values, and a mixture of node and atomic values”; atomic values – component types, nodes – hierarchies, node collections – grouping relationships; Niu, ¶70, “The xpath of a DOM tree node is defined as the string concatenating the tag's HTML tag (e.g., TITLE) and the tags of all its parents (e.g., HEAD and HTML)”; tags are metadata).
Further, Yuan as modified by Chen discloses wherein the grouping relationships and context information are determined using a predetermined word embedding model (Chen, p. 8, “First, for a sentence x to be translated, the encoder maps it into the source vector space and predicts the length L of the translated sentence”; in view of Chen, p. 5, “The semantic unit masking method based on the mutual information masks complete semantic units (words or phrases), so that the model is helped to learn the complete semantic information”; i.e., the encoder is a word embedding / vectorization model that maps sentence x to complete semantic units (words or phrases) describing semantic grouping relationships).
Regarding Claims 5, 12, and 19, Yuan discloses wherein the context information is selected from the group consisting of: a business domain, a category (Yuan, p. 10, “s21, performing word segmentation and labeling on the sentences in the website data obtained in the step S1 to obtain source language phrases, wherein the source language phrases after word segmentation and labeling are more suitable for the translation process”), a component type, a parent and a group.
Regarding Claims 7 and 14, Yuan as modified by Niu discloses wherein determining the component types, hierarchies and grouping relationships includes:
retrieving text hierarchical relationships from a text tree (Yuan, p. 10, “After the target website data is automatically captured, the webpage content can be analyzed by adopting an Xpath template”; Niu, ¶70, “The xpath of a DOM tree node is defined as the string concatenating the tag's HTML tag (e.g., TITLE) and the tags of all its parents (e.g., HEAD and HTML)”; i.e., retrieving a DOM tree to perform salient block identification algorithm);
finding text groupings according to the text hierarchical relationships (Niu, ¶69, identify relevant blocks such as text nodes corresponding to continuous text chunks; see Fig. 5, hierarchy #text);
performing special processing for at least some of the text elements (Niu, ¶69, perform a salient block identification algorithm; claim 9, identifying salient webpage blocks and salient extraction patterns to facilitate a preliminary translation extraction);
marking a sub-domain for each text group according to hierarchy and parent text (Niu, ¶69, concatenating HTML tag and tags of all its parents to define xpath of a DOM tree node); and
marking a type for each of the text elements (Niu, ¶69, identify each node as belonging to some pre-defined node types).
Allowable Subject Matter
Claims 6, 13, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims:
Yuan discloses using a translation engine to using the extracted text feature types to determine a re-ordering of translations of the text elements (p. 9, “The method comprises the steps of corpus cleaning, Chinese word segmentation, sentence and word alignment, language model and translation model learning, decoding, reordering and the like on bilingual resources, so that a Chinese and English scientific and technical information translation engine is constructed”).
Chen discloses using a translation engine to use the extracted text feature types to determine a re-ordering of translations of the text elements (p. 8, “During the reasoning process. First, for a sentence x to be translated, the encoder maps it into the source vector space and predicts the length L of the translated sentence. Note that at this point the encoder no longer performs the mask prediction process. The target input is then initialized at the target using a < MASK > sequence of length L. The decoder outputs a possible translation result according to the input and the information given by the encoder, masks the token with the confidence smaller than the threshold value in the translation result, and finally inputs the translation after the masking into the decoder for prediction again. The iteration is continued until the two outputs no longer change or the maximum number of iterations is reached”; per p. 5, “so as to solve the problems of insufficient modeling of source word representation and insufficient random masking of the target in iterative improvement of non-autoregressive machine translation”).
The priors of record, alone or in combination, do not disclose wherein using the extracted text feature types to determine a re-ordering of translations of the text elements includes:
inputting strings of text elements of a same one of the grouping relationships into a predetermined translation engine, wherein the strings are input with: information about the word vectors, an indication of text feature types, and associated portions of the context information;
obtaining outputs of the translation engine, wherein the outputs include target vectors for the strings of text elements;
obtaining target translations for the strings of text elements; and
re-ordering the strings of text elements according to the target translations.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2012/0005571 A1 discloses displaying a markup language document in an original language by rendering an internal representation of the document, sending a data structure containing the texts from the text nodes of the internal representation, as distinct data entities, to a translation service, replacing the texts with translated texts received from the translation service resulting in a translated representation, and displaying a first translation of the document by rendering the translated representation.
US 2021/0397944 A1 discloses automated categorization of structured textual content from a document object model encapsulation of the structured textual content, have a multidimensional vector associated with them, where the values of the various dimensions of the multidimensional vector are based on the textual content in the corresponding node, the visual features applied or associated with the textual content of the corresponding node, and positional information of the textual content of the corresponding node.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 08/04/2026
Prosecution Insights