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 .
Response to Arguments
Applicant's arguments filed 11/17/2025 have been fully considered but they are not persuasive.
101 Rejection
Applicant states (pp. 7) that the amended claim 1 limitation “identify at least one hundred of the other entigen groups” is not mentally performable. Examiner respectfully disagrees, because one can mentally observe or evaluate to iteratively determine various alternative meanings and sentiments of words in the context of surrounding text, albeit not most efficiently.
103 Rejection
Applicant states (pp. 8) that the amended claim 1 limitation “identify at least one hundred of the other entigen groups” is not taught by the cited prior art of record combined. Examiner respectfully disagrees, because Au takes a natural language input stream of symbols, and automatically disambiguates a contextual meaning (i.e., other entigen groups) for it [0018]. The number of contextual meanings of a natural language input is unlimited (i.e., at least one hundred) [0009]. The system can be repeatedly applied to multiple natural language input streams of symbols [0018].
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea as a mental process without significantly more.
Claim 1 is rejected under 35 U.S.C. 101.
Step 1
Claim 1 recites “A method”, which is directed to a statutory category.
Step 2A – Prong One
Claim 1 recites the following limitations directed to an abstract idea:
“for each different meaning…of the word to produce…identigens” recites an abstract idea as a mental process. One can mentally observe or evaluate by enumerating the various meanings of a word.
“interpret…pairs of sequentially adjacent identigens…to determine a most likely meaning interpretation…and produce an entigen group” recites an abstract idea as a mental process. One can mentally observe or evaluate by picking a most likely meaning of a word in the context of surrounding text.
“identify at least one hundred of the other entigen groups…that include…sentiment characteristic entigens…further corresponding to alternate phrases” recites an abstract idea as a mental process. One can mentally observe or evaluate to iteratively determine various (at least 100) alternative meanings and sentiments of words in the context of surrounding text.
“generate an incremental sentiment characteristic entigen” recites an abstract idea as a mental process. One can mentally observe or evaluate by determining sentiments of words in the context of surrounding text.
“update the entigen group to include the incremental sentiment characteristic entigen” recites an abstract idea as a mental process. One can mentally observe or evaluate by combining the meanings and sentiments of words in the context of surrounding text.
Step 2A – Prong Two
This judicial exception is not integrated into a practical application.
Claim 1 recites additional elements “processor”, “memory”, “computing device”, “knowledge database”, “intercommunication”, “query software” and “intelligence software”, which are high-level recitation of generic computer components and functions that represent mere instructions to apply on a computer per MPEP §2106.05(f).
Viewing the claim as a whole, nothing provides integration into a practical application.
Step 2B
Claim 1 includes additional element “access…only one node of a knowledge database”, which qualifies as “iv. Storing and retrieving information in memory”, and is recognized by the courts as well-understood, routine, and conventional activity per MPEP 2106.05(d)(II).
The conclusions on the mere instructions to apply the abstract idea using generic computer components and functions carry over and do not add significantly more or provide any "inventive concept".
In summary, claim 1 is not eligible. Claims 2-6 depend on claim 1 and recite the same abstract idea.
Step 2A Prong One
The following claims recite additional elements that are mentally performable.
Claim 4 recites detecting that the entigen group does not include, or includes an incorrect, incremental entigen, which recites an abstract idea as a mental process. One can mentally observe or evaluate to determine if the entigen group contains an incorrect entigen.
Claim 5 recites matching entigen groups when their permutations overlap in words, and detecting association of an entigen group with a sentiment, which recites an abstract idea as a mental process. One can mentally evaluate or judge to match and determine sentiment of an entigen group based on sentiment of words.
Claim 6 recites matching entigen groups based on sharing, and majority, of sentiment identifiers, which recites an abstract idea as a mental process. One can mentally evaluate or judge to match sentiment of an entigen group based on shared sentiment identifiers.
Step 2A Prong Two
Claim 3 recites additional element “request” and “search”, which are high-level recitation of generic computer components and functions that represent mere instructions to apply on a computer.
Step 2B
Claim 2 recites “output a representation…to a requesting entity”. Claim 3 recites “initiate a search when receiving a…request”. Both qualify as “i. Receiving or transmitting data over a network”, and is recognized by the courts as well-understood, routine, and conventional.
In summary, these dependent claims do not add any additional elements sufficient to make the claims non-abstract. Therefore, they are not eligible accordingly.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Au. US patent application 2003/0130976 [herein “Au”], and further in view of Dillard et al. US patent 8,554,701 [herein “Dillard”].
Claim 1 recites “A method for execution by a computing device, the method comprising: executing, by a processor, query software from a first non-transitory memory of the computing device causing the processor to access, utilizing each word of a string of words, only one node of a knowledge database for each different meaning of one or more different meanings of the word to produce a plurality of sets of identigens, wherein each set of identigens includes a corresponding set of the one node for each different meaning of a particular word of the string of words, wherein each identigen of the set of identigens includes a meaning identifier for a particular different meaning of the one or more different meanings, an instance identifier that includes a particular spelling in a particular language for the particular word, and”.
Au automatically disambiguates a contextual meaning of natural language input streams of symbols (i.e., words), by measuring the relative distance between symbols in a semantic network (i.e., knowledge database) [0083]. Au’s semantic network has 3 sets of nodes: (i) symbol nodes (i.e., instance identifiers) each representing a symbol (i.e., spelling of a word in a language) in input streams (i.e., queries), (ii) meaning nodes (i.e., meaning identifiers) each representing a distinct meaning, and (iii) context nodes a set of which representing contextual meaning; and nodes are connected by semantic links, e.g., a symbol node is connected to its meaning node(s) (i.e., identigen) [0018].
Claim 1 further recites “an interpretation applicability reference that declares when the particular different meaning is a legitimate applicable meaning of the particular word, wherein each meaning identifier associated with a particular set of identigens represents a different meaning of the one or more different meanings of a corresponding word of the string of words, wherein a particular interpretation applicability reference of a particular identigen only provides information when an interpretation of a human reaction of a corresponding particular different meaning of the one or more different meanings applies and does not include a timestamp of an event;”
The instant specification (pp. 56, lines 10-18) gives an example of timing requirements as a prepositional phrase for the next two hours in query “for the next two hours, which media sources are most associated with fake news pertaining to the Senate tax bill?”
Au utilizes prepositional symbols and phrases to disambiguate prepositional contextual shades of meaning (i.e., interpretation applicability reference), including logical prepositions ‘and’, ‘or’, and ‘not’ as well as time-related prepositions ‘from’, ‘to’, ‘after’ (i.e., not timestamp) [0098]. The optimal contextual distance function used (i.e., applied) to disambiguate is adjusted (i.e., declared) for the closest (i.e., legitimate) prepositional symbols and phrases (e.g., for the next two hours) in the context (fig. 15; [0166]).
Claim 1 further recites “executing, by the processor, intelligence software from a second non-transitory memory of the computing device to facilitate intercommunication between the query software and the intelligence software causing the processor to interpret, based on identigen pairing rules indicated by only one direct link between directly adjacent nodes of the knowledge database, pairs of sequentially adjacent identigens of adjacent sets of identigens of the plurality of sets of identigens to determine a most likely meaning interpretation of the string of words and produce an entigen group, wherein each entigen of the entigen group corresponds to a selected identigen of one of the plurality of sets of identigens having a selected meaning of the one or more different meanings of a corresponding word that represents a most likely meaning interpretation of the corresponding word of the string of words,”
Au’s semantic network has 3 sets of nodes – symbol/meaning/context nodes – and nodes are connected by semantic links to capture various candidate meanings of words in the query [0018]. In particular, the query is represented in the semantic network as a linked list of symbol nodes corresponding to the string of symbols in the query, one node connecting to (i.e., pairing with) the next (i.e., direct link) in the query (Claim 16). Au measures semantic distance by the aggregate path length of weighted semantic links [0147]. The sequence of meaning nodes (i.e., sequentially adjacent identigens) corresponding to the sequence of query symbol nodes with the minimum aggregate path length is chosen to represent the most likely meaning of the query (i.e., entigen group) [0018].
Claim 1 further recites “wherein each entigen of the entigen group represents a single conceivable and perceivable thing in space and time that is independent of language and corresponds to an interpretation applicability reference of a corresponding selected identigen associated with the entigen group,”
Au’s semantic network contains symbol nodes for natural language symbols, and meaning nodes that capture the meaning (i.e., conceivable and perceivable thing in space and time) of natural language symbols [0018].
Claim 1 further recites “wherein the entigen group is the most likely meaning interpretation of the string of words, wherein the knowledge database includes a plurality of records that link entigens having a connected meaning,”
Using Au’s semantic network of nodes and semantic links (i.e., records), meaning nodes that are semantically the closest to the query symbol nodes are chosen to collectively represent the first most likely meaning of the query [0018].
Claim 1 further recites “wherein each selected identigen that has a meaning identifier that represents the most likely meaning interpretation of the corresponding word favorably pairs with at least one corresponding sequentially adjacent identigen of an adjacent set of identigens in accordance with the identigen pairing rules of the knowledge database,”
Au’s semantic network has 3 sets of nodes – symbol/meaning/context nodes – and nodes are connected by semantic links to capture various candidate meanings of words in the query [0018]. In particular, the query is represented in the semantic network as a linked list of symbol nodes corresponding to the string of symbols in the query, one node connecting to the next in the query (Claim 16). Au measures semantic distance by the aggregate path length of weighted semantic links [0147]. The sequence of meaning nodes linked to the corresponding sequence of query symbol nodes with the minimum aggregate path length is chosen (i.e., favored) to represent the most likely meaning of the query [0018].
Claim 1 further recites “wherein other entigen groups share common entigens with the entigen group and represent meanings of permutations of the string of words; executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to identify at least one hundred of the other entigen groups from the knowledge database that include one or more sentiment characteristic entigens that are indicative of sentiment of a portion of a particular entigen group of the at least one hundred of the other entigen groups, the at least one hundred of the other entigen groups further corresponding to alternate phrases utilizing different permutations of words of the string of words,”
Au takes a natural language input stream of symbols, and automatically disambiguates a contextual meaning (i.e., other entigen groups) for it [0018]. The number of contextual meanings of a natural language input is unlimited (i.e., at least one hundred) [0009]. The system can be repeatedly applied to multiple natural language input streams of symbols [0018], each of which is, according to the Merriam-Webster dictionary, an ordered arrangement (i.e., permutation) of a set of objects (i.e., words).
Au does not disclose limitation “executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to generate an incremental sentiment characteristic entigen based on sentiment identifiers of the one or more sentiment characteristic entigens; and”.
However, Dillard determines sentence-level sentiments expressed in customer reviews, by pre-processing logical prepositions such as negation words together with interceding adverbs or enclosing phrases to create tokens (i.e., sentiment characteristic entigens) appropriately reflecting the associated sentiments (Dillard: 13:5-23), from which to generate the sentiment expressed (i.e., applied) in a sentence/phrase (i.e., entigen group) (Dillard: 4:66-5:19). The majority (i.e., incremental) sentiment of all sentences on a topic of an item is determined, and the sentences most relevant to the topic with that majority sentiment are selected (Dillard: 9:38-52). Topic assignment and sentiment determination is repeated iteratively for a collection of sentences (i.e., other entigen groups) (Dillard: 10:62-11:6).
Au does not disclose limitation “executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to update the entigen group to include the incremental sentiment characteristic entigen as an updated entigen group to provide clarity of the most likely meaning interpretation of the string of words.”
However, Dillard stores the most representative (i.e., most likely meaning interpretation) extracted quote sentences (i.e., entigen group) for later presentation to customers (Dillard: 10:11-18), together (i.e., updated) with corresponding sentiment indicators (i.e., sentiment identifiers) (Dillard: 10:44-52).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Dillard to Au. One having ordinary skill in the art would have found motivation to incorporate fine-grained sentiment meaning of Dillard in the semantic network of Au in addition to linguistic meaning in the context of the enclosing sentence/phrase.
Claim 2 recites “The method of claim 1 further comprising: executing, by the processor, further query software from the first non-transitory memory to facilitate intercommunication between the intelligence software and the query software causing the processor to: output a representation of the updated entigen group to a requesting entity; and facilitate storage of the updated entigen group in the knowledge database.”
Au and Dillard teach claim 1, where Dillard stores (i.e., updates) the most representative extracted quote sentences (i.e., entigen group) for later presentation (i.e., output) to customers (i.e., requesting entity) (Dillard: 10:11-18), together with corresponding sentiment indicators (Dillard: 10:44-52).
Claim 3 recites “The method of claim 1 further comprising: executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to: initiate a search of the knowledge database to identify the entigen group when a search timeframe has expired; and initiate the search of the knowledge database when receiving a sentiment identifier identification request from a requesting entity.”
Au and Dillard teach claim 1, where Dillard stores (i.e., identifies) the most representative extracted quote sentences (i.e., entigen group) for later presentation to customers (i.e., requesting entity) (Dillard: 10:11-18), together with corresponding sentiment indicators (i.e., sentiment identifiers) (Dillard: 10:44-52).
Au does not disclose this claim; however, If no sentiment is detected with a selected quote (i.e., search expired), Dillard enables customers to interact with it, such as to search for other items having extracted quotes with a positive/negative sentiment regarding the topic addressed by the selected quote (Dillard: 11:14-23).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Dillard to Au. One having ordinary skill in the art would have found motivation to incorporate fine-grained sentiment meaning of Dillard in the semantic network of Au in addition to linguistic meaning in the context of the enclosing sentence/phrase.
Claim 4 recites “The method of claim 1 further comprising: executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to: generate the incremental sentiment characteristic entigen when detecting that the entigen group does not include the incremental sentiment characteristic entigen; and generate the incremental sentiment characteristic entigen when detecting that the entigen group includes an incorrect sentiment characteristic entigen.”
Au and Dillard teach claim 1, where Dillard determines (i.e., detects) sentence-level sentiments expressed in customer reviews, by creating tokens (i.e., sentiment characteristic entigens) appropriately reflecting the associated sentiments (Dillard: 13:5-23), from which to generate the sentiment expressed in a sentence/phrase (Dillard: 4:66-5:19). Sentiment expressed in a sentence (i.e., entigen group) can be positive (i.e., including), negative (i.e., not including), mixed, or neutral (i.e., no sentiment) (Dillard: 4:66-5:9).
Au does not disclose this claim; however, to improve accuracy, terms (i.e., entigens) representing best indicators of sentiment are retained to avoid negatives-labeled-as-positives and positives-labeled-as-negatives errors (i.e., incorrect) (Dillard: 13:40-47).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Dillard to Au. One having ordinary skill in the art would have found motivation to incorporate fine-grained sentiment meaning of Dillard in the semantic network of Au in addition to linguistic meaning in the context of the enclosing sentence/phrase.
Claim 5 recites “The method of claim 1 further comprising: executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to: identify the at least one hundred of the other entigen groups from the knowledge database by at least one of: matching the entigen group to a first entigen group of the other entigen groups when at least some words of the string of words of the phrase are included in words of a first permutation of the string of words of a first permutation of the phrase that corresponds to the first entigen group; detecting that the first entigen group is associated with a first sentiment characteristic entigen of the one or more sentiment characteristic entigens; matching the entigen group to a second entigen group of the other entigen groups when at least some entigens of the entigen group are included in the second entigen group; and detecting that the second entigen group is associated with a second sentiment characteristic entigen of the one or more sentiment characteristic entigens.”
Au takes a natural language input stream of symbols, and automatically disambiguates a contextual meaning (i.e., entigen group) for it [0018]. The number of contextual meanings of a natural language input is unlimited (i.e., at least one hundred) [0009]. The system can be repeatedly applied to multiple natural language input streams of symbols [0018], each of which is an ordered arrangement (i.e., permutation) of a set of objects (i.e., words).
Claim 6 recites “The method of claim 1 further comprising: executing, by the processor, further intelligence software from the second non-transitory memory causing the processor to: generate the incremental sentiment characteristic entigen based on the sentiment identifiers of the one or more sentiment characteristic entigens by at least one of: determining a corresponding candidate incremental sentiment characteristic entigen of at least some of the at least one hundred of the other entigen groups that shares at least one of the sentiment identifiers the one or more sentiment characteristic entigens to produce a plurality of candidate incremental sentiment characteristic entigens; and determining the incremental sentiment characteristic entigen based on a majority of common sentiment identifiers of the plurality of candidate incremental sentiment characteristic entigens.”
Au takes a natural language input stream of symbols, and automatically disambiguates a contextual meaning (i.e., entigen group) for it [0018]. The number of contextual meanings of a natural language input is unlimited (i.e., at least one hundred) [0009]. The system can be repeatedly applied to multiple natural language input streams of symbols [0018].
Au and Dillard teach claim 1, where Dillard stores the most representative (i.e., common) extracted quote sentences for later presentation to customers (Dillard: 10:11-18), together (i.e., updated) with corresponding sentiment indicators (i.e., sentiment identifiers) (Dillard: 10:44-52).
Au does not disclose this claim; however, Dillard determines sentence-level sentiments expressed in customer reviews, by pre-processing logical prepositions such as negation words together with interceding adverbs or enclosing phrases to create tokens (i.e., sentiment characteristic entigens) appropriately reflecting the associated sentiments (Dillard: 13:5-23), from which to generate the sentiment expressed (i.e., applied) in a sentence/phrase (i.e., entigen group) (Dillard: 4:66-5:19). The majority (i.e., incremental) sentiment of all sentences on a topic of an item (i.e., other entigen group) is determined, and the sentences most relevant to the topic with that majority sentiment are selected (Dillard: 9:38-52).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Dillard to Au. One having ordinary skill in the art would have found motivation to incorporate fine-grained sentiment meaning of Dillard in the semantic network of Au in addition to linguistic meaning in the context of the enclosing sentence/phrase.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHELLY X. QIAN whose telephone number is (408)918-7599. The examiner can normally be reached Monday - Friday 8-5 PT.
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/SHELLY X QIAN/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154