DETAILED ACTION
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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,2,7,8,13 and 14 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by US 20190258717 A1; Williams; Frank John et al. (hereinafter Williams)
Regarding claim 1, Williams teaches A method for execution by a computing device, the method comprises: identifying a symbolic representation of a phrase to produce a plurality of tokens, wherein the symbolic representation includes representation of one or more of textual words, textual symbols of portions of word, images, and sounds (Williams [0004] This invention relates generally to computing systems and more particularly to generating data representations of data and analyzing the data utilizing the data representations. [0136] The element identification module 302 processes the formatted content 314 based on element rules 318 and an element list 332 to produce identified element information 340. Rules 316 includes the element rules 318 (e.g., match, partial match, language translation, etc.). Lists 330 includes the element list 332 (e.g., element ID, element context ID, element usage ID, words, characters, symbols etc.). The IEI control module 308 may provide the rules 316 and the lists 330 by accessing stored data 360 from a memory associated with the IEI module 122. Generally, an embodiment of this invention presents solutions where the stored data 360 may further include one or more of a descriptive dictionary, categories, representations of element sets, element list, sequence data, pending questions, pending request, recognized elements, unrecognized elements, errors, etc. [0170] FIG. 7B is a relationship block diagram illustrating an embodiment of relationships between things 510 and representations of things 512 within a computing system. The things 510 includes conceivable and perceivable things including actions 522, items 524, and attributes 526. The representation of things 512 includes representations of things used by humans 514 and representation of things used by of computing devices 516 of embodiments of the present invention. The things 510 relates to the representations of things used by humans 514 where the invention presents solutions where the computing system 10 supports mapping the representations of things used by humans 514 to the representations of things used by computing devices 516, where the representations of things used by computing devices 516 map back to the things 510.[0203] The processing of the content to produce the knowledge includes a series of steps. For example, a first step includes identifying words of an ingested phrase to produce tokenized words. As depicted in FIG. 8F... [0171] The representations of things used by humans 514 includes textual words 528, textual symbols 530, images (e.g., non-textual) 532, and other sensorial information 534 (e.g., sounds, sensor data, electrical fields, voice inflections, emotion representations, facial expressions, whistles, etc.). The representations of things used by computing devices 516 includes identigens 518 and entigens 520. The representations of things used by humans 514 maps to the identigens 518 and the identigens 518 map to the entigens 520. The entigens 520 uniquely maps back to the things 510 in space and time, a truest meaning the computer is looking for to create knowledge and answer questions based on the knowledge.[0204] A second step of the processing of the content to produce the knowledge includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in FIG. 8F, a specific example of the second step includes the element identification module 302 performing a look up of identigen identifiers, utilizing an element list 332 and in accordance with element rules 318, of the one or more identigens associated with each tokenized word of the formatted content 314 to produce identified element information 340.[0207] As depicted in FIG. 8F, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word [220-228] elaborate on the matter [FIG.8E-8G] shows the corresponding steps identifying a symbolic representation of a phrase to produce a plurality of tokens, wherein the symbolic representation includes representation of one or more of textual words, textual symbols of portions of word, images, and sounds) wherein each token of the plurality of tokens is associated with a corresponding identigen set such that each identigen of the corresponding identigen set represents a different meaning of one or more different meanings of a particular symbolic representation of the phrase (Williams [0171] The representations of things used by humans 514 includes textual words 528, textual symbols 530, images (e.g., non-textual) 532, and other sensorial information 534 (e.g., sounds, sensor data, electrical fields, voice inflections, emotion representations, facial expressions, whistles, etc.). The representations of things used by computing devices 516 includes identigens 518 and entigens 520. The representations of things used by humans 514 maps to the identigens 518 and the identigens 518 map to the entigens 520. The entigens 520 uniquely maps back to the things 510 in space and time, a truest meaning the computer is looking for to create knowledge and answer questions based on the knowledge.[0204] A second step of the processing of the content to produce the knowledge includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in FIG. 8F, a specific example of the second step includes the element identification module 302 performing a look up of identigen identifiers, utilizing an element list 332 and in accordance with element rules 318, of the one or more identigens associated with each tokenized word of the formatted content 314 to produce identified element information 340.[0204] A second step of the processing of the content to produce the knowledge includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in FIG. 8F, a specific example of the second step includes the element identification module 302 performing a look up of identigen identifiers, utilizing an element list 332 and in accordance with element rules 318, of the one or more identigens associated with each tokenized word of the formatted content 314 to produce identified element information 340. [0205] A unique identifier is associated with each of the potential object, the characteristic, and the action (OCA) associated with the tokenized word (e.g. sequential identigens). For instance, the element identification module 302 identifies a functional symbol for “the”, identifies a single identigen for “black”, identifies two identigens for “bat” (e.g., baseball bat and flying bat), identifies a single identigen for “eats”, and identifies a single identigen for “fruit.” When at least one tokenized word is associated with multiple identigens, two or more permutations of sequential combinations of identigens for each tokenized word result. For example, when “bat” is associated with two identigens, two permutations of sequential combinations of identigens result for the ingested phrase. [207-215 & 220-220] further elaborate on the matter [FIG.8E-8G] shows the wherein each token of the plurality of tokens is associated with a corresponding identigen set such that each identigen of the corresponding identigen set represents a different meaning of one or more different meanings of a particular symbolic representation of the phrase) generating a first equation package for the plurality of tokens that corresponds to a first permutation of a plurality of permutations of interpretation of the plurality of tokens based on the one or more different meanings of the symbolic representations of the phrase, (Williams [0206] A third step of the processing of the content to produce the knowledge includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens (e.g., relationships), where each sequential linking pairs a preceding identigen to a next identigen, and where an equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. Multiple OCAs occur for a common word when the word has multiple potential meanings (e.g., a baseball bat, a flying bat). [0207] As depicted in FIG. 8F, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word of the identified element information 340, the interpretation module 304 generates, in accordance with interpretation rules 320 and a groupings list 334, an equation package to include one or more of the identifiers of the tokenized words, a list of identifiers of the identigens of the equation package, a list of pairing identifiers for sequential pairs of identigens, and a quality metric associated with each sequential pair of identigens (e.g., likelihood of a proper interpretation). For instance, the interpretation module 304 produces a first equation package that includes a first identigen pairing of a black bat (e.g., flying bat with a higher quality metric level), the second pairing of bat eats (e.g., the flying bat eats, with a higher quality metric level), and a third pairing of eats fruit, and the interpretation module 304 produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit.[0215] The method continues at step 652 where the processing module, for each permutation of sequential combinations of identigens, generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module generates the equation package to include a plurality of equation elements...[224-230] elaborate on the matter [FIG.8F&8J] elaborate on the matter) wherein the first equation package includes a sequence of first selected equation elements representing the first permutation of the plurality of permutations of interpretation such that values of the first selected equation elements are dependent on a composite of values for the first permutation of the plurality of permutations of interpretation; (Williams [206] each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens (e.g., relationships), where each sequential linking pairs a preceding identigen to a next identigen, and where an equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. Multiple OCAs occur for a common word when the word has multiple potential meanings (e.g., a baseball bat, a flying bat). [0207] As depicted in FIG. 8F, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word of the identified element information 340, the interpretation module 304 generates, in accordance with interpretation rules 320 and a groupings list 334, an equation package to include one or more of the identifiers of the tokenized words, a list of identifiers of the identigens of the equation package, a list of pairing identifiers for sequential pairs of identigens, and a quality metric associated with each sequential pair of identigens (e.g., likelihood of a proper interpretation). For instance, the interpretation module 304 produces a first equation package that includes a first identigen pairing of a black bat (e.g., flying bat with a higher quality metric level), the second pairing of bat eats (e.g., the flying bat eats, with a higher quality metric level), and a third pairing of eats fruit, and the interpretation module 304 produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit. [0208] A fourth step of the processing of the content to produce the knowledge includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in FIG. 8F, a specific example of the fourth step includes the interpretation module 304 applying interpretation rules 320 (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce a number of permutations of the sequential combinations of identigens to produce interpreted information 344 that includes identification of at least one equation package as a surviving interpretation SI (e.g., higher quality metric level). [215-223] elaborate on the matter [FIG.8E-8G] elaborate on the matter) and updating, utilizing a knowledge database, the first equation package for the plurality of tokens that optimizes an interpretation confidence level for the plurality of tokens to produce a second equation package that includes a sequence of second selected equation elements that corresponds to a second permutation of the plurality of permutations of interpretation of the plurality of tokens, (Williams [207] a higher quality metric level), and a third pairing of eats fruit, and the interpretation module 304 produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit. [0208] A fourth step of the processing of the content to produce the knowledge includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in FIG. 8F, a specific example of the fourth step includes the interpretation module 304 applying interpretation rules 320 (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce a number of permutations of the sequential combinations of identigens to produce interpreted information 344 that includes identification of at least one equation package as a surviving interpretation SI (e.g., higher quality metric level). [0210] A fifth step of the processing of the content to produce the knowledge utilizing the confidence level includes integrating knowledge of the surviving equation package into a knowledge base. For example, integrating at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. As another example, the portion of the reduced OCA combinations may be translated into rows and columns entries when utilizing a rows and columns database rather than a graphical database. When utilizing the rows and columns approach for the knowledge base, subsequent access to the knowledge base may utilize structured query language (SQL) queries.[0306] The curating of the knowledge continues at step 795 where the scores are interpreted to determine whether the initial interpretation is reliable and, when reliable, adds the initial interpretation to a knowledge database. For example, a weighting approach is utilized to aggregate scores to produce a confidence level. For instance, weighting factors are multiplied by each component of the scores (e.g., an alignment component, the source reliability component, an information age component) to produce intermediate scores for aggregation to produce the conference level. The confidence level indicates that the initial interpretation is reliable when the confidence level is greater than a confidence threshold. [0311] A weighted scoring approaches applied to the scores for the entigen groups to produce a confidence level of the initial interpretation 801. For example, a sum of the scores of the confirming entigen groups 802 is multiplied by a confirming weighting factor to produce a confirming intermediate confidence level, the sum of the scores of the inconclusive entigen groups 804 is multiplied by an inconclusive weighting factor to produce an inconclusive intermediate confidence level, and a sum of the scores of the disconfirming entigen groups 806 is multiplied by a disconfirming weighting factor to produce a disconfirming intermediate conference level. The disconfirming intermediate confidence level is subtracted (e.g., to lower an overall conference level) from a sum of the confirming intermediate confidence level and the inconclusive intermediate confidence level to produce the overall confidence level. [328-335] further elaborate [FIG.8A-8F] show corresponding visual) wherein optimization of the interpretation confidence level for the second equation package indicates a higher probability of a correct interpretation than other permutations of interpretation of the plurality of tokens from the knowledge database such that the second permutation of the plurality of permutations of interpretation of the plurality of tokens represents a most likely interpretation of the phrase. (Williams [0153] The grouping interpretation module 406 interprets the validated groupings information 416 based on the question information 346 and in accordance with the interpretation rules 320 to produce interpreted information 344 (e.g., most likely interpretations, next most likely interpretations, etc.). For example, the grouping interpretation module 406 obtains context, obtains favorable historical interpretations, processes the validated groupings based on interpretation rules 320, where each interpretation is associated with a correctness likelihood level. [0306] The curating of the knowledge continues at step 795 where the scores are interpreted to determine whether the initial interpretation is reliable and, when reliable, adds the initial interpretation to a knowledge database. For example, a weighting approach is utilized to aggregate scores to produce a confidence level. For instance, weighting factors are multiplied by each component of the scores (e.g., an alignment component, the source reliability component, an information age component) to produce intermediate scores for aggregation to produce the conference level. The confidence level indicates that the initial interpretation is reliable when the confidence level is greater than a confidence threshold. [0311] A weighted scoring approaches applied to the scores for the entigen groups to produce a confidence level of the initial interpretation 801. For example, a sum of the scores of the confirming entigen groups 802 is multiplied by a confirming weighting factor to produce a confirming intermediate confidence level, the sum of the scores of the inconclusive entigen groups 804 is multiplied by an inconclusive weighting factor to produce an inconclusive intermediate confidence level, and a sum of the scores of the disconfirming entigen groups 806 is multiplied by a disconfirming weighting factor to produce a disconfirming intermediate conference level. The disconfirming intermediate confidence level is subtracted (e.g., to lower an overall conference level) from a sum of the confirming intermediate confidence level and the inconclusive intermediate confidence level to produce the overall confidence level.[0329] When the confidence level of the initial interpretation compares unfavorably to a confidence threshold, the method branches to step 818 (e.g., the processing module indicates the unfavorable comparison when the confidence level is less than the confidence threshold). When the conference level of the initial interpretation compares favorably to the confidence threshold, the method continues at step 816 (e.g., the processing module indicates the favorable comparison when the confidence level is greater than the confidence threshold [330-336] further elaborate [FIG.8F-8K] show corresponding visual)
Corresponding system claim 7 is rejected similarly as claim 1 above. Additional Limitations: Device with processor(s) and memory (Williams [FIG.1 in conjunction with FIG.3] show the corresponding Device with processor(s) and memory [84-86 & 413] show the corresponding Device with processor(s) and memory)
Corresponding product claim 13 is rejected similarly as claim 1 above. Additional Limitations: computer readable medium capable of reading and executing instructions (Williams [FIG.1 in conjunction with FIG.3] show the corresponding Device with processor(s) and memory with computer readable medium capable of reading and executing instructions [84-86 & 413] show the corresponding Device with processor(s) and memory with computer readable medium capable of reading and executing instructions)
Regarding claim 2, Williams teaches The method of claim 1 further comprises: updating, utilizing the knowledge database, the second equation package for the plurality of tokens that further optimizes the interpretation confidence level for the plurality of tokens to produce a third equation package that includes a sequence of third selected equation elements that corresponds to a third permutation of the plurality of permutations of interpretation of the plurality of tokens (Williams [0207] As depicted in FIG. 8F, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word of the identified element information 340, the interpretation module 304 generates, in accordance with interpretation rules 320 and a groupings list 334, an equation package to include one or more of the identifiers of the tokenized words, a list of identifiers of the identigens of the equation package, a list of pairing identifiers for sequential pairs of identigens, and a quality metric associated with each sequential pair of identigens (e.g., likelihood of a proper interpretation). For instance, the interpretation module 304 produces a first equation package that includes a first identigen pairing of a black bat (e.g., flying bat with a higher quality metric level), the second pairing of bat eats (e.g., the flying bat eats, with a higher quality metric level), and a third pairing of eats fruit, and the interpretation module 304 produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit.[0215] The method continues at step 652 where the processing module, for each permutation of sequential combinations of identigens, generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module generates the equation package to include a plurality of equation elements, where each equation element describes the relationship (e.g., describes, acts on, is a, belongs to, did, did too, etc.) between sequentially adjacent identigens of a plurality of sequential combinations of identigens. Each equation element may be further associated with a quality metric to evaluate a favorability level of an interpretation in light of the sequence of identigens of the equation package. [0222] As depicted in FIG. 8J, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word of the identified element information 340, generating the equation packages in accordance with interpretation rules 320 and a groupings list 334 to produce a series of equation elements that include pairings of identigens. For instance, the interpretation module 304 generates a first pairing to describe a black animal, a second pairing to describe an animal that flies, a third pairing to describe flies and eats, a fourth pairing to describe eats fruit, and a fifth pairing to describe eats fruit and insects. [0228] For each permutation of sequential combinations of identigens, the method continues at step 657 where the processing module generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module includes all other permutations of all other tokenized words to generate the equation packages. Each equation package includes a plurality of equation elements describing the relationships between sequentially adjacent identigens of a plurality of sequential combinations of identigens. [328-335] further elaborate [FIG.8A-8J] show corresponding visual) wherein the sequence of third selected equation elements includes an interpretation time reference that provides time information when an interpretation of a particular corresponding different meaning of the corresponding one or more different meanings applies for a corresponding token (Williams [139] The answer quality level 356 includes a determination of a quality level of the preliminary answers 354 based on the answer rules 322. The inferred question information 352 may further be associated with time information 348, where the time information includes one or more of current real-time, a time reference associated with entity submitting a request, and a time reference of a collections response. [0173] Each identigen 518 is associated with an identigens identifier (IDN) 536. The IDN 536 includes a meaning identifier (ID) 538 portion, an instance ID 540 portion, and a type ID 542 portion. The meaning ID 538 includes an identifier of common meaning. The instance ID 540 includes an identifier of a particular word and language. The type ID 542 includes one or more identifiers for actenyms, itenyms, attrenyms, singulatums, pluratums, a time reference, and any other reference to describe the IDN 536. The mapping of the representations of things used by humans 514 to the identigens 518 by the computing system of the present invention includes determining the identigens 518 in accordance with logic and instructions for forming groupings of words.[0179] The groupings table 584 includes multiple fields including grouping ID 586, word strings 588, identigens 518, and entigens 520. The computing system of the present invention may produce the groupings table 584 as a stored fact base for valid and/or invalid groupings of words identified by their corresponding identigens. For example, the valid grouping “pilot Tom” referring to flyer Tom the person is represented with a grouping identifier of 3001 and identity and identifiers 150.001 and 457.001. The entigen field 520 may indicate associated entigens that correspond to the identigens. For example, entigen e717 corresponds to the flyer pilot meaning and entigen e61 corresponds to the time the person meaning. Alternatively, or in addition to, the entigen field 520 may be populated with a single entigen identifier (ENI).[0322] A second score generating approach includes determining an aging score for the first entigen group based on an age of the first phrase. For example, the processing module obtains a freshness level (e.g., a timestamp of generation of the first phrase, a timestamp of receipt of the first phrase, a timeframe between generation of the phrase and a current time, a timeframe between receipt of the phrase and a current time) [254,302, 323] further elaborate ) wherein the further optimization of the interpretation confidence level for the third equation package indicates a still higher probability of the correct interpretation than other permutations of interpretation of the plurality of tokens from the knowledge database such that the third permutation of the plurality of permutations of interpretation of the plurality of tokens represents an updated most likely interpretation of the phrase. (Williams [0306] The curating of the knowledge continues at step 795 where the scores are interpreted to determine whether the initial interpretation is reliable and, when reliable, adds the initial interpretation to a knowledge database. For example, a weighting approach is utilized to aggregate scores to produce a confidence level. For instance, weighting factors are multiplied by each component of the scores (e.g., an alignment component, the source reliability component, an information age component) to produce intermediate scores for aggregation to produce the conference level. The confidence level indicates that the initial interpretation is reliable when the confidence level is greater than a confidence threshold. [0311] A weighted scoring approaches applied to the scores for the entigen groups to produce a confidence level of the initial interpretation 801. For example, a sum of the scores of the confirming entigen groups 802 is multiplied by a confirming weighting factor to produce a confirming intermediate confidence level, the sum of the scores of the inconclusive entigen groups 804 is multiplied by an inconclusive weighting factor to produce an inconclusive intermediate confidence level, and a sum of the scores of the disconfirming entigen groups 806 is multiplied by a disconfirming weighting factor to produce a disconfirming intermediate conference level. The disconfirming intermediate confidence level is subtracted (e.g., to lower an overall conference level) from a sum of the confirming intermediate confidence level and the inconclusive intermediate confidence level to produce the overall confidence level [0315] A second generating step includes interpreting, based on a knowledge database, the plurality of sets of identigens to produce the first entigen group. Each entigen of the first entigen group corresponds to a selected identigen of one of the plurality of sets of identigens that represents a most likely meaning of a corresponding word of the at least some of the words of the string of words. The first entigen group is a most likely meaning of the string of words. The knowledge database includes a plurality of records that link words having a connected meaning. For example, a graphical database is utilized to represent entigens and linkages between the entigens [0329] When the confidence level of the initial interpretation compares unfavorably to a confidence threshold, the method branches to step 818 (e.g., the processing module indicates the unfavorable comparison when the confidence level is less than the confidence threshold). When the conference level of the initial interpretation compares favorably to the confidence threshold, the method continues at step 816 (e.g., the processing module indicates the favorable comparison when the confidence level is greater than the confidence threshold [330-336] further elaborate [FIG.8F-8K] show corresponding visual)
Corresponding system claim 8 is rejected similarly as claim 2 above.
Corresponding product claim 14 is rejected similarly as claim 2 above.
Allowable Subject Matter
Claims 3-6,9-12, and 15-18 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. Claims 3-6,9-12, and 15-18 have allowable subject matter, the claims contain the unique concept of finding inconsistency clarification associated with conflicting interpretation time references between specialized equation packages for the plurality of tokens, wherein each token of the plurality of tokens is associated with a corresponding identigen. This unique step in combination with the surrounding language around the correspond claim helps give the claims novelty, however all claims are objected to since they are dependent on rejected claims as shown above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARYAN D TOUGHIRY whose telephone number is (571)272-5212. The examiner can normally be reached Monday - Friday, 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ARYAN D TOUGHIRY/Examiner, Art Unit 2165
