DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The amendment filed on March 31, 2026 has been entered. The following has occurred: Claims 1-3, 5-11, 13-19, and 21-23 are amended; Claims 4, 12, and 20 are cancelled; Claims 21-23 are newly added.
Claims 1-4, 5-11, 13-19, and 21-23 are pending.
Response to Amendment
35 U.S.C. 101 rejection is maintained in light of the amendment.
Claim interpretation is maintained.
Claim Objections have been withdrawn.
35 U.S.C. 112(a) rejection has been withdrawn in light of the amendment.
Claim Interpretation
Claims 17-19, 22, and 23 are directed to “a computer-readable medium.” The “medium” as recited in the claims is interpreted in light of applicant’s specification description [0094]. The office interprets the claims recite “a computer-readable medium” to be “a non-transitory computer-readable medium”.
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 1-4, 5-11, 13-19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-8 are directed to a method (i.e., a process), claims 9-16 are directed to a system (i.e., a machine), and 17-20 are directed to a computer product (i.e., an article of manufacture). Thus, the eligibility analysis proceeds to Step 2A. prong one.
Step 2A. prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
While claims 1 and 17, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1 and 17, is
receiving a first trademark pair comprising an attribute of a first trademark and a corresponding attribute of a second trademark;
determining a level of similarity between the first trademark pair and respective second trademark pairs included in respective legal proceedings maintained in a database, each of the respective legal proceedings pertaining to whether a likelihood of confusion exists between its respective second trademark pair;
selecting, from the database, a subset of legal proceedings from the legal proceedings, each legal proceeding of the subset including at least one respective second trademark pair that has the level of similarity with the first trademark pair that meets a threshold condition;
generating a prediction score as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark by:
determining, based on the subset of legal proceedings, a set of confusion scores corresponding to each legal proceeding of the subset of legal proceedings and a next closest legal proceeding of the subset of legal proceedings,
generating, based on the set of confusion scores, a first feature vector comprising a set of first features corresponding to the set of confusion scores,
generating, based on the subset of legal proceedings, a second feature vector by concatenating second features of the subset of legal proceedings,
providing the first and the second feature vectors as an input to a machine learning model that outputs the prediction score based on the first and second feature vectors; and
providing the prediction score to a user.
The abstract idea recited in claim 9 is,
receive, a first trademark pair comprising an attribute of a first trademark and a corresponding attribute of a second trademark,
determine a level of similarity between the first trademark pair and respective second trademark pairs included in first respective legal proceedings maintained in a database, each of the respective first legal proceedings pertaining to whether a likelihood of confusion exists between its respective second trademark pair,
select from the database, a subset of legal proceedings from the first legal proceedings, each legal proceeding of the subset including at least one respective second trademark pair that has the level of similarity with the first trademark pair that meets a threshold condition,
generate a prediction score as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark by:
determining, based on the subset of legal proceedings, a set of confusion scores corresponding to each legal proceedings of the subset of legal proceedings a next closest legal proceeding of the subset of legal proceedings,
generating, based on the set of confusion scores, a first feature vector comprising a set of first features corresponding to the set of confusion scores, and provide the first feature vector as an input to a machine learning model, causing the machine learning model to output the prediction score based on the first feature vector, and
provide the prediction score to a user interface.
The claimed invention is directed to an abstract idea of predicting trademark similarity.
The limitations above suggest a process similar to collecting information (limitations [A], [H], [J]), analyzing the information (limitations [B]-[H], [K]-[O]) and presenting information (limitation [I], [P]). Because the limitations above closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the same claim limitations above recite commercial or legal interactions that include legal obligations and business relations. Under the broadest reasonable interpretation, other than the additional elements of computer components, the limitations recite a process of collecting information for trademark pair comprising attributes; determining a level of similarity between the trademark pairs including respective legal proceedings; selecting legal proceedings to determine likelihood of confusion between trademarks. This is described in the applicant’s specification para. [0002] that the assessing and determining whether a particular trademark is similar to another trademark is performed by an administrative agency or judicial body. Trademark Examiners and Attorneys have been responsible for collecting and reviewing trademark with legal proceedings to find similarities, before the invention of computer existed. Because these limitations above closely follow the steps standard in commercial or legal interaction that includes legal obligations and business relations for reviewing and predicting trademark similarity, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Additionally and alternatively, the claims recite steps involving the determination of a prediction score that would find a likelihood of confusion between trademarks to be directed to “Mathematical Concept” category of the abstract ideas. Under the broadest reasonable interpretation, other than the additional elements of computer components, the steps in limitations [D]-[H] and [M]-[O] of generating feature vectors and inputting vectors in a machine learning model to output a prediction score (i.e., likelihood score) amount to forms of performing mathematical calculations, which falls under “mathematical concept” of the abstract idea.
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer.
The terminology for “determiner,” “extractor,” and “featurizer” can be representative of a person, however, the Office interprets these to be lexigraphy name of computer functions.
The additional elements considered include:
Claim 1: “performed by a computer-implemented trademark similarity prediction engine comprising a trademark similarity determiner, a feature extractor, and a featurizer,” “by the trademark similarity determiner,” “by the feature extractor,” “by the featurizer,” “by the computer-implemented trademark similarity prediction engine,”;
Claim 9: “system for predicting trademark similarity, comprising: a computer-implemented model trainer comprising: a first processor circuit and a first memory that stores first program code configured to be executed by the first processor circuit, the first program code configured to, when executed by the first processor circuit, cause the system to:” “a computer-implemented trademark similarity prediction engine comprising a trademark similarity determiner, a feature extractor, and a featurizer, the computer-implemented trademark similarity prediction engine further comprising: a second processor circuit; and; “by the trademark similarity determiner,” “by the feature extractor,” “by the featurizer,” and “wherein the computer-implemented trademark similarity prediction engine,”
Claim 17: “computer-readable storage medium having program instructions recorded thereon that, when executed by at least one processor of a computer-implemented trademark similarity prediction engine comprising a trademark similarity determiner, a feature extractor, and a featurizer”; “by the trademark similarity determiner,” “by the feature extractor,” “by the featurizer,” “by the computer-implemented trademark similarity prediction engine,”;
The additional element of a system comprising generic computer elements are found to recite mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer to transmit, manipulate, and display information is not an improvement to a technology or technical field. The additional elements merely recite computer elements to receive, determine, select, generate, and provide information. Nothing is presented as to how the memory is improved or how the computer system used is improved by the claimed invention. The additional element is recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components, i.e., these generic computing elements are merely being used to perform the tasks of the abstract idea, see MPEP 2106.05(f). There is no indication from the specification that the computer elements are anything but generic hardware and/or software, and the combination of elements is simply a generic computing system (see Applicant’s Specification at least at paragraphs [0073]-[0082] indicating the computer system can be any generic computer device comprising a number of components that are generic and indiscriminate device (e.g., processor, memory, and user interface). In paragraph [0025] describes similarity prediction engine 102 and legal proceeding database are implemented using one or more server computers or computing devices. Also, in paragraph [0050] of applicant’s specification indicates, “trademark similarity prediction engine 202 is implemented as a web-based service, the web-based service may be accessible via a browser application executing on computing device 206.” Then Fig. 2 and para. [0033] indicates, “trademark similarity prediction engine 202 comprises a trademark similarity determiner 212, a feature extractor 214, a featurizer 216, and a prediction model 218.” The Office interprets trademark similarity prediction engine to be application software executed on computer devices, and “trademark similarity determiner 212, a feature extractor 214, a featurizer 216, and a prediction model 218” are lexigraphy name of the computer functions).
That is, the function of limitations [A]-[P] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer.
Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations of [A]-[P] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claims as a whole merely describes how to generally “apply” the concept for predicting trademark similarity. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2-3, 10-11, and 18-19, the claims further recite additional abstract steps of generating second feature vector; generating third feature vector; and determining distance between feature vectors, which does not change the abstract idea indicated in the independent claims. The steps are recited at a high level of generality (i.e., as a generic computer system performing generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
As for dependent claims 5, 6, 13, and 14, the claims further recite additional descriptive information regarding to each legal proceeding of the subset of legal proceedings. The additional descriptive information does not change the abstract idea of the independent claims. No additional element has been recited. The claims are ineligible.
In dependent claims 6 and 14 further recite additional abstract steps of wherein the machine learning model is generated by generating third feature vector and determining respective distance between the third feature vectors. The abstract idea of the claims is not changed from the independent claims. The steps are recited at a high level of generality (i.e., as a generic computer system performing generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
As for dependent claims 7, 8, 15, 16, and 23 the claims further recite additional descriptive information regarding to features and attribute. The additional descriptive information does not change the abstract idea of the independent claims. No additional element has been recited. The claims are ineligible.
As for dependent claims 21 and 22, the claims further recite additional abstract steps of mathematical function for concatenating second features for second feature vector to be provided to the machine learning model. The abstract idea of the claims is not changed from the independent claims. The steps are recited at a high level of generality (i.e., as a generic computer system performing generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Therefore, claims 1-4, 5-11, 13-19, and 21-23 are rejected under 35 U.S.C. 101.
Allowable Subject Matter over Prior Art
The closest prior art found are:
Keyngnaert et al. (US 20160260033 A1) is directed to system and method for identifying similar trademarks from one or more repositories based on training a goods and/or services similarity engine to identify similarities between pairs of descriptions of goods and/or services in a corpus of training data that includes the descriptions of goods and/or services for registered trademarks and trademark classes associated with the descriptions of goods and/or services.
Keyngnaert teaches, receiving a first trademark pair comprising an attribute of a first trademark and a corresponding attribute of a second trademark (Claim 1, para. [0061], [0068], [0079], and [0088]);
determining a level of similarity between the first trademark pair and respective second trademark pairs included in respective legal proceedings maintained in a database, each of the respective legal proceedings pertaining to whether a likelihood of confusion exists between its respective second trademark pair (para. [0088]-[0089] disclosing legal analyzer in conjunction of similarity engine for determining similarity in trademark with respective legal proceedings of trademark trial and appeal board proceedings);
selecting, from the database, a subset of legal proceedings from the legal proceedings, each legal proceeding of the subset including at least one respective second trademark pair that has the level of similarity with the first trademark pair that meets a threshold condition (para. [0016], [0086]-[0087], and [0112]);
While Keyngnaert teaches assigning vector value for training data includes description of goods and/or service, however, Keyngnaert does not teach the specific configuration of generating feature vector based on each legal proceeding of the subset of legal proceedings from the trademark pairs, and inputting the feature vector of legal proceedings to a machine learning model that outputs a prediction score, based on the first feature vector, as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark.
Anderson (US 20170322983 A1) is directed to system and method for receiving search results, each including at least one ascertainable text-based property and a numeric score indicative of a measure of similarity between the search result and the proposed trademark.
Anderson teaches receiving a first trademark pair comprising an attribute of a first trademark and a corresponding attribute of a second trademark (para. [0033] disclosing retrieving information relating to trademarks from various databases. Para. [0034] disclosing search result containing attributes of corresponding trademarks. In para. [0044], the system retrieves search results responsive to a query including a proposed trademark including one or more search parameters. search parameters include one or more databases to be searched, one or more classes, one or more keywords corresponding to goods or services, one or more statuses, one or more owner names, one or more dates, etc. Which are representative of attribute of trademark. In para. [0045] search results include existing or expired trademarks. In para. [0046] disclosing search query for a proposed trademark “sam” and example search result “Samir”, which is representative of a first trademark pair comprising an attribute of a first trademark and a corresponding attribute of a second trademark);
determining a level of similarity between the first trademark pair and respective second trademark pairs maintained in a database, each of the respective legal proceedings pertaining to whether a likelihood of confusion exists between its respective second trademark pair (para. [0044] “the search results may be retrieved in response to multiple queries. In an embodiment, the query or queries may include a proposed trademark including one or more search parameters. The search parameters can include information to help narrow the scope of a trademark clearance investigation. Example search parameters include one or more databases to be searched, one or more classes, one or more keywords corresponding to goods or services, one or more statuses, one or more owner names, one or more dates, etc.” The multiple queries are representative of searching first trademark pair and second trademark pairs. In Para. [0045] disclosing search results include existing or expired trademarks in databases. In para. [0048]-[0050] disclosing the determining of level of similarity between search query of proposed mark with search result with numeric score which is representative of likelihood of confusion between respective trademark pairs. In para. [0049] discloses “determining numeric score indicative of a measure of relevance between the search result and the proposed trademark of the query, where relevance is a measure of similarity and additional factors based on the one or more search parameters” which is representative of determining a level of similarity between the first trademark pair and respective second trademark pairs);
selecting, from the database, including at least one respective second trademark pair that has the level of similarity with the first trademark pair that meets a threshold condition (In para. [0078]-[0079], [0081] and Fig. 4, control bar 450, and Fig. 5 filter panel 510, discloses selecting or filtering various database sources and control bar that filter a numeric value for a threshold condition for the level of similarity with trademark pairs).
However, Anderson does not teach the specific configuration of generating feature vector based on each legal proceeding of the subset of legal proceedings from the trademark pairs, and inputting the feature vector of legal proceedings to a machine learning model that outputs a prediction score, based on the first feature vector, as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark.
Xu (US 20180114091 A1) is directed to a trademark retrieval method, comprising: establishing a sample trademark library and establishing a correlation between sample trademarks and division data for figurative element codes of known pending or registered figurative trademarks; extracting and processing image feature information about the sample trademarks, and establishing a correlation between the sample trademarks and the extracted image feature information; extracting image feature information about a trademark to be retrieved; carrying out matching retrieval by taking the image feature information as a retrieval condition, and finding out a sample trademark reaching a pre-determined similarity degree, and a sample trademark with the highest similarity degree and a corresponding figurative element code; acquiring and confirming a figurative element code of the trademark to be retrieved; taking the figurative element code as a retrieval condition to carry out matching retrieval, and finding out a matching sample trademark; collecting a result retrieved by taking the image feature information as the retrieval condition and a result retrieved by taking the figurative element code as the retrieval condition; and sequencing the collected trademarks according to the similarity degree of the image feature information.
However, Xu does not teach the specific configuration of generating feature vector based on each legal proceeding of the subset of legal proceedings from the trademark pairs, and inputting the feature vector of legal proceedings to a machine learning model that outputs a prediction score, based on the first feature vector, as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark.
Gross et al. (US 20160180480 A1) is directed to system and method for analyzing post-grant challenge proceedings initiated in a governmental agency that are associated with patents, which teaches wherein a feature vector is derived for content of a formal patent challenge request presented in said second pending proceeding and is used to identify a data cluster of one or more similar first completed patent challenge proceedings (in claim 9).
However, Gross does not teach specific configuration of applying of feature vector of legal proceedings from trademark pair as input to a machine learning model that outputs a prediction score, based on the first feature vector, as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark.
Nefedov et al. (US 20160350294 A1) is directed to method and system delivering graph-based metric to measure a similarity between weighted sets of classifications codes (presented as nodes) defined on hierarchical taxonomy trees. The suggested method is applied to find company peers in a particular domain, e.g., the IP domain based on a company patent portfolio.
However, Nefedov does not teach the specific configuration of generating feature vector based on each legal proceeding of the subset of legal proceedings from the trademark pairs, and inputting the feature vector of legal proceedings to a machine learning model that outputs a prediction score, based on the first feature vector, as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark.
However, the combination of the above references does not explicitly teach the limitations of claims 1, 9, and 17: generating a prediction score as to whether a subsequent legal proceeding would find a likelihood of confusion between the first trademark and the second trademark by: determining, by the featurizer and based on the subset of legal proceedings, a set of confusion scores corresponding to each legal proceeding of the subset of legal proceedings and a next closest legal proceeding of the subset of legal proceedings, generating, by the featurizer and based on the set of confusion scores, a first feature vector comprising a set of first features corresponding to the set of confusion scores, generating, by the featurizer and based on the subset of legal proceedings, a second feature vector by concatenating second features of the subset of legal proceedings, and providing, by the featurizer, the first and second feature vectors as an input to a machine learning model that outputs the prediction score based on the first and second feature vectors (i.e. in the particular manner it is claimed in the context of the whole claim is not disclosed, taught or suggested in the prior art(s)).
Examiner notes that the underlined limitations above, in combination with the other limitations found within the independent claims are found to be allowable over the prior art of record.
The prior art of record neither anticipates nor fairly and reasonably teach the independent claims 1, 9, and 17.
Examiner notes the application is not in condition for allowance, given the outstanding rejection under 35 U.S.C. 101.
Response to Remarks
35 U.S.C. 112(a) Rejection:
The Applicant has cancelled the rejected claim limitation, therefore the 112(a) rejection has been withdrawn.
35 U.S.C. 101 Rejection:
The Applicant’s remarks are fully considered, however are found to be unpersuasive.
On pages 14-16, the Applicant asserts the amended claim limitation are not directed to concepts performed in the human mind, towards a mental process.
The Examiner respectfully disagrees. The amended claim limitations comprise a plurality of steps for generating a prediction score for likelihood using mathematical calculations, which falls specifically under “mathematical concept” of the abstract idea as well as human evaluation under “mental processes” grouping of the abstract ideas.
On page 16, the Applicant asserts the claim is like Synopsys. The Examiner respectfully disagrees. Synopsys recites a claim to a specific data encryption method for computer communication involving a several-step manipulation of data, which is cannot be practically performed in the human mind. The claimed invention recites several-steps of mathematical calculation unrelated to encryption method tied to computer communication. The assertion is found to be unpersuasive.
On pages 17-18, the Applicant asserts practical application that improves the functioning of a computer device and further provided paragraph [0022] and [0023] of the specification for recitation of intended result of utilizing less compute resources. The recitation of “wherein utilizing the machine learning model to generate the prediction score based on the first feature vector utilizes fewer computer resources than generating the prediction score based on the actual text of the subset of legal proceedings” is intended result of the application but does the specification nor the claim describes how the claimed invention produces or utilizes fewer computer resources.
Thus, Examiner respectfully disagrees. The suggested improvement of a computer operation by generating the prediction score in a manner that results reduction in computing resource required is intended by the applicant but not reflected in the claim nor the specification. The specification fails to provide disclosure for generating the prediction score that directly reduces the amount of compute resource other than conclusory statement of the intended result.
In paragraph [0003] of the Applicant’s specification indicates a business problem for laborious and time-consuming process in manually searching for published legal proceedings, which can be performed by a person (paragraph [0022]), to be applied on a computer. The Applicant fails to provide persuasive argument for the “improvement to other technology or technical field.” That is, as reflected in Enfish, there is a fundamental difference between computer functionality improvements (improvement of the technology or technical field), on the one hand, and uses of existing computers as tools to perform a particular task (collecting, analyzing, and displaying information), on the other. The alleged advantages that the Applicant touts do not concern an improvement to computer capabilities or any machinery but instead relate to an alleged improvement in transmitting, analyzing, and determining information for a desirable result, which a computer is used as a mere tool in its ordinary capacity, see MPEP 2106.05(f). To further clarify, the applicant reflected a business need/reason of the abstract idea for the analyzing and generating similarity prediction of trademarks for clients. The computer and software, itself is merely used “applied” for the expected result of efficiency, convenience and time/cost saving. The claims do not reflect an improvement to the technology of the computer functionalities other than, by using the additional elements of the computer system, desired result can be produced without a doubt and concern to technological details for how it is done. That is, the computer system itself or specific technology is not improved in anyway other than being applied as a tool/instrument for the judicial exception (abstract idea).
For these reasons above, the 101 rejection is maintained.
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Higgins (US 20090190839 A1) is directed to computer-implemented method, system, and computer program product for generating vector-based similarity scores in text document comparisons considering confounding effects of document length. Vector-based methods for comparing the semantic similarity between texts (such as Content Vector Analysis and Random Indexing) have a characteristic which may reduce their usefulness for some applications: the similarity estimates they produce are strongly correlated with the lengths of the texts compared. The statistical basis for this confound is described, and suggests the application of a pivoted normalization method from information retrieval to correct for the effect of document length. In two text categorization experiments, Random Indexing similarity scores using pivoted normalization are shown to perform significantly better than standard vector-based similarity estimation methods.
Bayardo et al. (US 8041694 B1) is directed to a system and method for comparing vector in a set and estimating similarity for a similarity score meeting a similarity threshold,
F. Mohd Anuar, R. Setchi and Y. -K. Lai, "Trademark retrieval based on phonetic similarity," 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA, 2014, pp. 1642-1647, doi: 10.1109/SMC.2014.6974151; teaching an algorithm to retrieve phonetically similar trademarks that can be used as a means for supporting trademark examination during the registration process. The algorithm employs a phonology based string similarity algorithm together with a typography mapping and token rearrangement to compute a phonetic similarity between trademarks.
Conclusion
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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/WENREN CHEN/Primary Examiner, Art Unit 3626
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