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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 13 January 2025 is/are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, and mutatis mutandis claims 17 and 18, the limitation “associating logic and inference through Knowledge Record to Term and Knowledge Record to Knowledge Record linkages” is unclear. Claim 1 recites “associating logic and inference…” at lines 8-9. However, the intended relationship established by “associating” is unclear. Based on the plain language of the claims, the association of logic and inference is factually established in the definition of the words. More specifically, logic is necessarily associated with inference, as an inference is a logical extrapolation from existing data. With logic and inference already being associated, it is unclear what further association is being claimed.
If the claim intends the association to exist between “logic and inference” and some other claim part, it is unclear what that claim part might be. The remaining components of the limitation are “through Knowledge Record to Term and Knowledge Record to Knowledge Record linkages.” This portion of the limitation indicates two separate claim parts “Knowledge record to Term…linkages” or “Knowledge record to Knowledge record linkages”, where each of these claim parts are understood as a medium of some kind (i.e., through…) which facilitates the association. However, the facilitation of that association does not clarify the targets of the same. Therefore, claims 1, 17, and 18 lack clarity and are rejected.
Regarding claim 2, claim 2 recites the limitation “the user” in line 3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 6, the phrase “such as” renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Regarding claims 2-16, claims 2-16 depend from claim 1 and incorporate all limitations therefrom. Therefore, claims 2-16 are rejected for at least the same reasons as described above with reference to claim 1.
Appropriate correction is required.
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.
Claim(s) 1, 4-7, 9, 11-15, and 17-18 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Ritchie (U.S. Pat. App. Pub. No. 2014/0280210, hereinafter Ritchie).
Regarding claim 1, Ritchie discloses A method for contextual inference and response generation in natural language (Systems and methods described with reference to the “disclosed method of natural language processing”; Ritchie, ¶ [0030]), the method comprising: accessing a data structure, comprising a plurality of Knowledge Records, Terms, and Relationship Types (“the server 108 is configured to store, in the memory 114, a graph data structure 118 including a plurality of nodes 132, each node 132 associated with an entity data value 136, and a plurality of links 134” where the graph data structure corresponds to the data structure, the “entity data value 136” corresponds to the claimed “terms”, and the “relationship data values” correspond to the claimed relationship types. Further, the combination of linked nodes corresponds to the claimed “knowledge records”; Ritchie, ¶ [0031]); using states of knowledge to facilitate interaction (Discloses “traversing the graph data structure to identify one or more problems indicated by the evaluation criteria-rating pair values, in response to the traversing, determining at one or more changes to the graph data structure to satisfy one or more identified problems, if the determination is affirmative, populating a solution graph data structure”; Ritchie, ¶ [0046]); invoking relationship type properties for inference (Discloses the “graph traversal routine 126 would locate ‘apple’ and through further query to the metadata database 120, determine that apple ‘is a type of’ (relationship) ‘food’,” which is understood as teaching the invocation of the specific property of a relationship (in this case, the “is a type of” property) to infer a connection between the user’s need (e.g., “food” to cure hunger) and a specific entity (e.g., “apple”).; Ritchie, ¶ [0039]); classifying Terms through Knowledge Records for response generation (Discloses parsing input strings (e.g., “I”) and classifying them into specific semantic entity types (e.g., “a ‘person’ entity data value 136”) defined in the database (knowledge records) and, using the parsed and classified (identified) input, the system populates “the graph data structure… with the identified entity data values”; Ritchie, ¶ [0033], [0046]); associating logic and inference through Knowledge Record to Term and Knowledge Record to Knowledge Record linkages (Discloses “the processor 116 queries the metadata database 120 with the ‘person’− ‘possesses’ −’hunger’ problem (data entity relationship) and further query ‘hunger’ to determine that ‘food’ can have a reducing effect on hunger (a correlation between ‘food’ and ‘hunger’ based on the nominal evaluation criteria-rating pair between these two entities in the metadata database 120),” thus associating logic (identifying a problem and a solution) by traversing linkages between terms (person to hunger) and knowledge records (hunger requires food, Apple is food), where the traversal of linkages constitutes the claimed association of logic and inference.; Ritchie, ¶ [0037], [0039]); generating or selecting responses based on Criteria-Value Rating Pairs and user Traits (Discloses “populating the graph data structure... includes applying evaluation criteria-rating pair values” where “the rating values 144 can correspond to personal individual values documented in the metadata database 120. For example, rating values 144 can be assigned to evaluation criteria label values 142 to reflect how an individual values concepts such as “family”, “honour”, “punctuality”, etc. {User traits}” which can be used to calculate the “highest net value” response.; Ritchie, ¶ [0034], [0046]); incorporating learnings comprising insights, adjustments to algorithmic parameters, and constructed models that result from the processing, analysis, and interpretation of user interactions and external data sources, into the data structure (“According to one example, the client application 112 that is loaded on the electronic device 102 (operated by the user) provides a “questionnaire” or online form to assist with populating the graph data structure 118, including clarification and updates of the metadata database 120 {algorithmic parameters} where new entity data values 136, relationship data values 138, or evaluation criteria-rating pair values 140 can be “learned”. A wide variety of machine learning or artificial intelligence techniques can be utilized.” As well the system includes a “crawling engine” that processes data from the “internet” or “enterprise or institutional databases” and can “identify and “learn” new entity data values, relationship values and evaluation criteria-rating pair values” as incorporated into a “solution graph data structure”; Ritchie, ¶ [0025], [0039]-[0040], [0043]); selecting responses based on location information (“the rating values 144 can correspond to nominal human values (or regional or cultural human values) that may be pre-populated in the metadata database 120” where regional human values is location information.; Ritchie, ¶ [0034]); and storing interaction graphs (“the processor 116 determines a solution graph data structure and stores it in the memory 114” and can also store “transmissions including input messages and response messages.”; Ritchie, ¶ [0021], [0039]); wherein the method is performed by a processing unit of a computer system (“The server 108 stores, in the memory 114, a plurality of computer readable instructions executable by the processor 116” and when “the processor 116 executes the instructions of application 104, the processor 116 is configured to perform various functions specified by the computer readable instructions of the application 104” which includes performance of the described method.; Ritchie, ¶ [0020]).
Regarding claim 4, Ritchie discloses further comprising using real-time intelligent search to complement the data structure (“the electronic device 104 can be a crawling engine (not shown in FIG. 1). A crawling engine is a server or application that provides functionality for automated “bot” or web crawling of data sources, in the case of the Internet or a database query processor in the case of an intranet, or an enterprise or institutional database system”; Ritchie, ¶ [0025]) when existing Knowledge Records are insufficient for generating appropriate responses, (The existence of the search indicates that existing available information was considered insufficient for generating an appropriate response by the system, where the root word “sufficient” is understood broadly to include a subjective determination regarding available data.; Ritchie, ¶ [0025]) wherein the search is conducted across internet or intranet data sources. (The “web crawling of data sources” can be performed across “the Internet or… an intranet” such as “an enterprise or institutional database system”; Ritchie, ¶ [0025]).
Regarding claim 5, Ritchie discloses wherein generating or selecting responses based on Criteria-Value Rating Pairs involves adjusting response selection based on the emotional impact indicated by the Criteria-Value Rating Pairs (Discloses that “Problems are identified or detected by reference to the evaluation criteria-rating pair values 140. In one example, a negative rating value indicates a problem” where “a negative rating value” indicating “a problem” corresponds to a negative emotional impact. The system selects the response that resolves this problem (maximizing net value), thereby adjusting selection based on the impact.; Ritchie, ¶ [0037]).
Regarding claim 6, Ritchie discloses wherein the Criteria-Value Rating Pairs are further used to adjust conversational tone based on identified user personality traits, such as empathy or ethical considerations (“rating values 144 can be assigned to evaluation criteria label values 142 to reflect how an individual values concepts {identified user personality traits} such as ‘family’, ‘honour’, ‘punctuality’, etc.” which are empathy or ethical considerations, and “The motivation data value 168 can change the logic provided to the processor 116 to create and format of the responses to the input that are suitable for the user and his/her situation”; Ritchie, ¶ [0034], [0045]).
Regarding claim 7, Ritchie discloses wherein storing interaction graphs includes the long-term retention of conversational contexts to facilitate the resumption of interactions with users at future points (“The memory 114 can also store transmissions including input messages and response messages between one or more of the electronic devices 102 and the server 108” and “extensive contextual situation or even domains of knowledge can be efficiently captured in the memory 114” such that “the graph data structure 118 can be populated with data values to represent various scenarios”; Ritchie, ¶ [0021], [0036]).
Regarding claim 9, Ritchie discloses wherein incorporating learnings into the data structure comprises refining algorithmic parameters to enhance the system’s accuracy and responsiveness over time (“provides techniques for adjusting or changing the contents of the metadata database 120 according to machine learning”; Ritchie, ¶ [0044]) based on feedback loops from user interactions (“the client application 112 that is loaded on the electronic device 102 (operated by the user) provides a “questionnaire” or online form to assist with populating the graph data structure 118, including clarification and updates of the metadata database 120 where new entity data values 136, relationship data values 138, or evaluation criteria-rating pair values 140 can be ‘learned’,” where the iteratively described input from the questionnaire or online form is a feedback loop and said loop is based on {from} user interactions.; Ritchie, ¶ [0043]).
Regarding claim 11, Ritchie discloses comprising dynamically generating personalized prompts or questions (“the client application 112 that is loaded on the electronic device 102 (operated by the user) provides a “questionnaire” or online form... including clarification and updates of the metadata database 120 where new entity data values 136, relationship data values 138, or evaluation criteria-rating pair values 140 can be ‘learned’.”; Ritchie, ¶ [0043]) based on gaps identified in the Knowledge Records (The “ ‘questionnaire’ or online form” is provided “to assist with populating the graph data structure 118 including clarification… of the metadata database 120” where populating the graph data structure for clarification is based on gaps identified in the Knowledge Records.; Ritchie, ¶ [0043]).
Regarding claim 12, Ritchie discloses further comprising analyzing sentiment of user inputs to tailor responses (“Problems are identified or detected by reference to the evaluation criteria-rating pair values 140. In one example, a negative rating value indicates a problem”; Ritchie, ¶ [0037]), where the sentiment analysis helps to determine the emotional state of the user for generating empathetically aligned responses (“the graph traversal routine 126 calculates, based on the net value of each graph data structure 118 on the subject ‘person’ and calculates, through analysis of the evaluation criteria-value rating pairs 140, the best or optimal solution based on highest net value to ‘person’,” where the negative rating is understood as a negative sentiment from the user (e.g., indicating like or dislike) as “problems” such as with respect to “pre-determined individual personal values” are identified when “the evaluation criteria-rating pair values is a negative value” and said sentiment analysis helps to determine the emotional state of the user.; Ritchie, ¶ [0040], [0049]).
Regarding claim 13, Ritchie discloses further comprising a mechanism for automatic update and expansion of the Knowledge Records (“the electronic device 104 can be a crawling engine...[providing] functionality for automated ‘bot’ or web crawling of data sources…[to] ‘learn’ new entity data values”; Ritchie, ¶ [0025]) based on emerging trends and vocabularies identified from the broader internet or intranet sources (These new entity data values {emerging trends and vocabularies} are identified from “Internet or... Intranet” sources; Ritchie, ¶ [0025]).
Regarding claim 14, Ritchie discloses wherein responses are selected based on the analysis of user interaction history (“the rating values 144 can correspond to personal individual values documented in the metadata database 120” where the ratings values are applied in determining a response to the user. Thus, the system selects a response, based on a correspondence determined between {the analysis} the response and documented personal individual values {user interaction history}; Ritchie, ¶ [0034], [0041]) to predict user needs or questions before they are explicitly stated (“In cases where no apparent solutions can be constructed from the graph data structure 118, the processor further queries the metadata database 120 in order to determine or identify more general solutions” thus the crawling engine can autonomously “identify problems” in a database without a user asking a question.; Ritchie, ¶ [0042]).
Regarding claim 15, Ritchie discloses further comprising employing user feedback on responses to refine and improve response accuracy and relevance (“the client application 112 that is loaded on the electronic device 102 (operated by the user) provides a “questionnaire” or online form... including clarification and updates of the metadata database 120 where new entity data values 136, relationship data values 138, or evaluation criteria-rating pair values 140 can be ‘learned’,” which refines and improves response accuracy and relevance; Ritchie, ¶ [0043]).
Regarding claim 17, Ritchie discloses A system for contextual inference and response generation in natural language (Systems and methods described with reference to the “disclosed method of natural language processing”; Ritchie, ¶ [0015], [0030]), comprising: a memory storing instructions; and a processor configured to execute the instructions to (“Computer-readable code executable by at least one processor 116 of the server 108 to perform the method can be stored in a computer-readable storage medium, such as a non-transitory computer-readable medium.”; Ritchie, ¶ [0030]): access a data structure, comprising a plurality of Knowledge Records, terms, and Relationship Types (“the server 108 is configured to store, in the memory 114, a graph data structure 118 including a plurality of nodes 132, each node 132 associated with an entity data value 136, and a plurality of links 134” where the graph data structure corresponds to the data structure, the “entity data value 136” corresponds to the claimed “terms”, and the “relationship data values” correspond to the claimed relationship types. Further, the combination of linked nodes corresponds to the claimed “knowledge records”; Ritchie, ¶ [0031]); use states of knowledge to facilitate interaction (Discloses “traversing the graph data structure to identify one or more problems indicated by the evaluation criteria-rating pair values, in response to the traversing, determining at one or more changes to the graph data structure to satisfy one or more identified problems, if the determination is affirmative, populating a solution graph data structure”; Ritchie, ¶ [0046]); invoke relationship type properties for inference (Discloses the “graph traversal routine 126 would locate ‘apple’ and through further query to the metadata database 120, determine that apple ‘is a type of’ (relationship) ‘food’,” which is understood as teaching the invocation of the specific property of a relationship (in this case, the “is a type of” property) to infer a connection between the user’s need (e.g., “food” to cure hunger) and a specific entity (e.g., “apple”).; Ritchie, ¶ [0039]); classify terms through Knowledge Records for response generation (Discloses parsing input strings (e.g., “I”) and classifying them into specific semantic entity types (e.g., “a ‘person’ entity data value 136”) defined in the database (knowledge records) and, using the parsed and classified (identified) input, the system populates “the graph data structure… with the identified entity data values”; Ritchie, ¶ [0033], [0046]); associate logic and inference through knowledge record to term and knowledge record to Knowledge Record linkages (Discloses “the processor 116 queries the metadata database 120 with the ‘person’− ‘possesses’ −’hunger’ problem (data entity relationship) and further query ‘hunger’ to determine that ‘food’ can have a reducing effect on hunger (a correlation between ‘food’ and ‘hunger’ based on the nominal evaluation criteria-rating pair between these two entities in the metadata database 120),” thus associating logic (identifying a problem and a solution) by traversing linkages between terms (person to hunger) and knowledge records (hunger requires food, Apple is food), where the traversal of linkages constitutes the claimed association of logic and inference.; Ritchie, ¶ [0037], [0039]); generate or selecting responses based on Criteria-Value Rating Pairs and user Traits (Discloses “populating the graph data structure... includes applying evaluation criteria-rating pair values” where “the rating values 144 can correspond to personal individual values documented in the metadata database 120. For example, rating values 144 can be assigned to evaluation criteria label values 142 to reflect how an individual values concepts such as “family”, “honour”, “punctuality”, etc. {User traits}” which can be used to calculate the “highest net value” response.; Ritchie, ¶ [0034], [0046]); incorporate learnings comprising insights, adjustments to algorithmic parameters, and constructed models that result from the processing, analysis, and interpretation of user interactions and external data sources, into the data structure (“According to one example, the client application 112 that is loaded on the electronic device 102 (operated by the user) provides a “questionnaire” or online form to assist with populating the graph data structure 118, including clarification and updates of the metadata database 120 {algorithmic parameters} where new entity data values 136, relationship data values 138, or evaluation criteria-rating pair values 140 can be “learned”. A wide variety of machine learning or artificial intelligence techniques can be utilized.” As well the system includes a “crawling engine” that processes data from the “internet” or “enterprise or institutional databases” and can “identify and “learn” new entity data values, relationship values and evaluation criteria-rating pair values” as incorporated into a “solution graph data structure”; Ritchie, ¶ [0025], [0039]-[0040], [0043]); selecting responses based on location information (“the rating values 144 can correspond to nominal human values (or regional or cultural human values) that may be pre-populated in the metadata database 120” where regional human values is location information.; Ritchie, ¶ [0034]); and store interaction graphs (“the processor 116 determines a solution graph data structure and stores it in the memory 114” and can also store “transmissions including input messages and response messages.”; Ritchie, ¶ [0021], [0039]).
Regarding claim 18, Ritchie discloses At least one non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to (Systems and methods described with reference to the “disclosed method of natural language processing” as implemented using “Computer-readable code executable by at least one processor 116 of the server 108 to perform the method can be stored in a computer-readable storage medium, such as a non-transitory computer-readable medium.”; Ritchie, ¶ [0030]): access a data structure, including a plurality of Knowledge Records, terms, and Relationship Types (“the server 108 is configured to store, in the memory 114, a graph data structure 118 including a plurality of nodes 132, each node 132 associated with an entity data value 136, and a plurality of links 134” where the graph data structure corresponds to the data structure, the “entity data value 136” corresponds to the claimed “terms”, and the “relationship data values” correspond to the claimed relationship types. Further, the combination of linked nodes corresponds to the claimed “knowledge records”; Ritchie, ¶ [0031]); use states of knowledge to facilitate interaction (Discloses “traversing the graph data structure to identify one or more problems indicated by the evaluation criteria-rating pair values, in response to the traversing, determining at one or more changes to the graph data structure to satisfy one or more identified problems, if the determination is affirmative, populating a solution graph data structure”; Ritchie, ¶ [0046]); invoke relationship type properties for inference (Discloses the “graph traversal routine 126 would locate ‘apple’ and through further query to the metadata database 120, determine that apple ‘is a type of’ (relationship) ‘food’,” which is understood as teaching the invocation of the specific property of a relationship (in this case, the “is a type of” property) to infer a connection between the user’s need (e.g., “food” to cure hunger) and a specific entity (e.g., “apple”).; Ritchie, ¶ [0039]); classify terms through Knowledge Records for response generation (Discloses parsing input strings (e.g., “I”) and classifying them into specific semantic entity types (e.g., “a ‘person’ entity data value 136”) defined in the database (knowledge records) and, using the parsed and classified (identified) input, the system populates “the graph data structure… with the identified entity data values”; Ritchie, ¶ [0033], [0046]); associate logic and inference through knowledge record to term and knowledge record to knowledge record linkages (Discloses “the processor 116 queries the metadata database 120 with the ‘person’− ‘possesses’ −’hunger’ problem (data entity relationship) and further query ‘hunger’ to determine that ‘food’ can have a reducing effect on hunger (a correlation between ‘food’ and ‘hunger’ based on the nominal evaluation criteria-rating pair between these two entities in the metadata database 120),” thus associating logic (identifying a problem and a solution) by traversing linkages between terms (person to hunger) and knowledge records (hunger requires food, Apple is food), where the traversal of linkages constitutes the claimed association of logic and inference.; Ritchie, ¶ [0037], [0039]); generate or select responses based on Criteria-Value Rating Pairs and user traits (Discloses “populating the graph data structure... includes applying evaluation criteria-rating pair values” where “the rating values 144 can correspond to personal individual values documented in the metadata database 120. For example, rating values 144 can be assigned to evaluation criteria label values 142 to reflect how an individual values concepts such as “family”, “honour”, “punctuality”, etc. {User traits}” which can be used to calculate the “highest net value” response.; Ritchie, ¶ [0034], [0046]); incorporate learnings comprising insights, adjustments to algorithmic parameters, and constructed models that result from the processing, analysis, and interpretation of user interactions and external data sources, into the data structure (“According to one example, the client application 112 that is loaded on the electronic device 102 (operated by the user) provides a “questionnaire” or online form to assist with populating the graph data structure 118, including clarification and updates of the metadata database 120 {algorithmic parameters} where new entity data values 136, relationship data values 138, or evaluation criteria-rating pair values 140 can be “learned”. A wide variety of machine learning or artificial intelligence techniques can be utilized.” As well the system includes a “crawling engine” that processes data from the “internet” or “enterprise or institutional databases” and can “identify and “learn” new entity data values, relationship values and evaluation criteria-rating pair values” as incorporated into a “solution graph data structure”; Ritchie, ¶ [0025], [0039]-[0040], [0043]); select responses based on location information (“the rating values 144 can correspond to nominal human values (or regional or cultural human values) that may be pre-populated in the metadata database 120” where regional human values is location information.; Ritchie, ¶ [0034]); and store interaction graphs (“the processor 116 determines a solution graph data structure and stores it in the memory 114” and can also store “transmissions including input messages and response messages.”; Ritchie, ¶ [0021], [0039]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ritchie as applied to claim 1 above, and further in view of Vasylyev (U.S. Pat. App. Pub. No. 2024/0412720, hereinafter Vasylyev).
Regarding claim 2, the rejection of claim 1 is incorporated. Ritchie discloses all of the elements of the current invention as stated above. Ritchie further discloses further comprising using … [artificial intelligence models] to enhance the generation or selection of responses, (Though not expressly described with reference to LLMs, Ritchie teaches the use of a “wide variety of machine learning or artificial intelligence techniques can be utilized” as part of the “adjusting or changing” of “the contents of the metadata database 120 according to machine learning or artificial intelligence approaches.” Since the metadata database and the data structure are the direct sources used to generate the response (i.e., they are traversed to find the solution graph), utilizing AI to “adjust or change” these databases directly enhances the generation of the response.; Ritchie, ¶ [0043]-[0045]) wherein the [AI model] uses past conversations or written examples from the user as input for response consistency (Teaches that “memory 114 can also store transmissions including input messages and response messages”{past conversations} and describes using “questionnaires” {written examples} or forms to “learn” new values from the user.; Ritchie, ¶ [0021], [0043]). However, Ritchie fail to expressly recite wherein the AI model is a large language model.
Vasylyev teaches an “AI assistant system with the capacity to monitor and record conversations, contextually interpret the recorded conversations and commands or requests, and respond based on the recorded conversation.” (Vasylyev, ¶ [0003]). Regarding claim 2, Vasylyev teaches wherein the AI model is a large language model (“The software components of assistant system 2 further includes a natural language processing unit 212” which can implement a “LLM to perform tokenization, encoding, contextual understanding, decoding, and detokenization of the conversation.”; Vasylyev, ¶ [0132], Provisional 62/472,292, ¶ [0016]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the natural language processing systems of Ritchie to incorporate the teachings of Vasylyev to include wherein the AI model is a large language model. Ritchie describes a system that parses input to populate a graph, but Vasylyev explicitly identifies that such conventional approaches often fail to maintain context over time. (Vasylyev, ¶ [0007]). “By actively updating the contextual understanding as each spoken utterance is processed” natural language processing systems incorporating the AI assistant of Vasylyev, are “able to provide much faster responses and more accurate, responsive, and contextualized assistance,” as recognized by Vasylyev. (Vasylyev, ¶ [0007], [0139]).
Regarding claim 16, the rejection of claim 1 is incorporated. Ritchie discloses all of the elements of the current invention as stated above. Ritchie further discloses further comprising providing contextual and ontological information, comprising Criteria-Value Rating Pairs, to third-party systems through an API interface, (“The electronic device can be a crawling engine” and “can be configured to transmit, to a solution database for further evaluation, a response to the input” where the “network interface device 110 allows the server 108 to communicate with other computing devices... via a link with the network 106” where a network interface that accepts inputs from a software bot (crawling engine) and returns structured data (responses) is understood as an API interface.; Ritchie, ¶ [0017], [0025], [0051]) wherein the method includes: formatting the accessed Knowledge Records, Terms, Relationship Types and Criteria-Value Rating Pairs into structured data payloads suitable for third-party integration (“populating a solution graph data structure that satisfies one or more identified problems, and transmitting, to the electronic device, a response to the input” and the “graph data structure 118 refers to a collection of nodes, links, and evaluation criteria-rating pairs that can be represented or stored in the memory 114 as data variables, arrays, fields, and pointers,” thereby generating the solution graph data structure which corresponds to the “structured data payload, and contains both the knowledge records (nodes/links) and Criteria value rating pairs. Further, since the structure is transmitted to an external “solution database” for “further evaluation” it is understood to be formatted in a way that is suitable for third-party integration.; Ritchie, ¶ [0021], [0046]); transmitting these data payloads to third-party systems comprising [artificial intelligence (AI) systems] (“The electronic device can be a crawling engine” and “can be configured to transmit, to a solution database for further evaluation, a response to the input” and “machine learning or artificial intelligence techniques can be utilized”; Ritchie, ¶ [0043], [0051]). However, Ritchie fail to expressly recite wherein the AI systems are Large Language Models (LLMs); receiving requests from the third-party systems for specific information based on the third-party system’s current contextual analysis and user interaction needs; selecting and sending enriched Knowledge Records and associated Criteria-Value Rating Pairs in response to the requests, aiding the third-party systems in generating contextually relevant and personalized responses.
The relevance of Vasylyev is described above with relation to claim 2. Regarding claim 16], Vasylyev teaches wherein the AI systems are Large Language Models (LLMs) (“The software components of assistant system 2 further includes a natural language processing unit 212” which can implement a “LLM to perform tokenization, encoding, contextual understanding, decoding, and detokenization of the conversation.”; Vasylyev, ¶ [0132]; See Provisional 62/472,292, ¶ [0016]); receiving requests from the third-party systems for specific information based on the third-party system’s current contextual analysis and user interaction needs (Assistant system 2 analyzes the conversation to identify gaps, it “evaluates the context…to identify any gaps in information that may necessitate accessing external sources” and “The assistant system 2 further constructs a query tailored to extract the necessary information…this may involve creating specific search terms, input parameters, or API requests” where the external service receives this specific API request to provide the data.; Vasylyev, ¶ [0365]-[0367]; See Provisional 62/472,292, ¶ [00164]); selecting and sending enriched Knowledge Records and associated Criteria-Value Rating Pairs in response to the requests, aiding the third-party systems in generating contextually relevant and personalized responses (The external resource selects the requested data and transmits it back to the assistant system 2, which is described by way of an example, as “FoodOrderingHub’s API… returns an order confirmation response, including an order ID and estimated delivery time” where the data sent back is structured and enriched, described in a second example as “collected information on historical company budgets, price ranges of key components...and comparative market analyses” and the “extracted information snippets may then be used to augment the original user command and the stored conversation context” for producing “a coherent and informative response based on the augmented context”; Vasylyev, ¶ [0147]-[0148], [0419], [0441]; See Provisional 62/472,292, ¶ [00164]-[00165]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the natural language processing systems of Ritchie to incorporate the teachings of Vasylyev to include wherein the AI systems are Large Language Models (LLMs); receiving requests from the third-party systems for specific information based on the third-party system’s current contextual analysis and user interaction needs; selecting and sending enriched Knowledge Records and associated Criteria-Value Rating Pairs in response to the requests, aiding the third-party systems in generating contextually relevant and personalized responses. Ritchie describes a system that parses input to populate a graph, but Vasylyev explicitly identifies that such conventional approaches often fail to maintain context over time. (Vasylyev, ¶ [0007]). “By actively updating the contextual understanding as each spoken utterance is processed” natural language processing systems incorporating the AI assistant of Vasylyev, are “able to provide much faster responses and more accurate, responsive, and contextualized assistance,” as recognized by Vasylyev. (Vasylyev, ¶ [0007], [0139]).
Claims 3, 8, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ritchie as applied to claim 1 above, and further in view of Bradley (U.S. Pat. App. Pub. No. 2017/0116184, hereinafter Bradley).
Regarding claim 3, the rejection of claim 1 is incorporated. Ritchie discloses all of the elements of the current invention as stated above. Ritchie further discloses wherein classifying terms through Knowledge Records comprises categorizing [conversational data values] and [regional or cultural human values] to support culturally and linguistically appropriate response generation (“The response can be calculated and formatted according to a motivation data value” selected from “a conversational data value” where conversational data includes greetings and “the rating values 144 can correspond to nominal human values (or regional or cultural human values) that may be pre-populated in the metadata database 120” which is the categorization of the regional or cultural human values to support culturally and linguistically appropriate response generation.; Ritchie, ¶ [0048]). However, Ritchie fails to expressly recite wherein conversational data values comprises greetings and wherein regional or cultural human values comprises languages.
Bradley teaches systems and methods of device personalization and localization. (Bradley, ¶ [0001], [0005]). Regarding claim 3, Bradley teaches wherein conversational data values comprises greetings (“the device 300 identifies the name ‘John’ as a personal contact of the user” and “may extract the name from the detected speech and compare the name to one or more contact names stored in a contact database” then “the device 300 determines the extracted name ‘John’ belongs to the ‘Friends’ contact group, which is assigned to an English language UI.”; Bradley, ¶ [0036]-[0037]) and wherein regional or cultural human values comprises languages (“The locale database 102 is a database storing locale data corresponding to one or more country or geographic region locales. The locale data includes, but is not limited to, language dictionaries”; Bradley, ¶ [0027]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the natural language processing systems of Ritchie to incorporate the teachings of Bradley to include wherein conversational data values comprises greetings and wherein regional or cultural human values comprises languages. The device described in Bradley automatically adapts based on “locale data” including “language, numerals, currency, unit of measurements, time, holiday events, weather, etc.,” such that the “the device can be dynamically transitioned into a different locale without requiring the user to manually manipulate a specific UI setting,” which provides the known benefit of increased user convenience and improved data personalization, as recognized by Bradley. (Bradley, ¶ [0015]-[0016]).
Regarding claim 8, the rejection of claim 1 is incorporated. Ritchie discloses all of the elements of the current invention as stated above. Ritchie further discloses further comprising … [including in] the data structure... user-contributed data (“the rating values 144 can correspond to personal individual values documented in the metadata database 120”; Ritchie, ¶ [0034]). However, Ritchie fail(s) to expressly recite further comprising securing access and interaction with the data structure, safeguarding the privacy and integrity of user-contributed data.
The relevance of Bradley is described above with relation to claim 3. Regarding claim 8, Bradley teaches further comprising securing access and interaction with the data structure, safeguarding the privacy and integrity of user-contributed data (“the device 100 is configured to perform real-time fascial and mouth recognition... and identify a spoken language based on the recognition operation” such that the system can determine “a sender or recipient” of communications, as would be necessary for application of user specific information (e.g., knowledge of names for stored “personal contacts of the user” and associated assignment “of a particular language”).; Bradley, ¶ [0024], [0029]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the natural language processing systems of Ritchie to incorporate the teachings of Bradley to include further comprising securing access and interaction with the data structure, safeguarding the privacy and integrity of user-contributed data. The device described in Bradley automatically adapts based on “locale data” including “language, numerals, currency, unit of measurements, time, holiday events, weather, etc.,” such that the “the device can be dynamically transitioned into a different locale without requiring the user to manually manipulate a specific UI setting,” which provides the known benefit of increased user convenience and improved data personalization, as recognized by Bradley. (Bradley, ¶ [0015]-[0016]).
Regarding claim 10, the rejection of claim 1 is incorporated. Ritchie disclose all of the elements of the current invention as stated above. Ritchie further discloses further comprising adjusting responses based on… [time information] to ensure temporal relevance, wherein responses are selected to align with the user’s likely activities at specific times (“rating values 144 can be assigned to evaluation criteria label values 142 to reflect how an individual values concepts such as... ‘punctuality’, etc.” which are empathy or ethical considerations, and the “motivation data value 168 can change the logic provided to the processor 116 to create... the responses to the input that are suitable for the user and his/her situation” where, in the context of punctuality, the time of day with regards to punctuality (punctuality is understood in the context of a schedule of events, where a person may be early, late, or on time to said events) indicates suitability to the user as well as likely activities at a specific time (a scheduled event has a specific start time).; Ritchie, ¶ [0034], [0045]). However, Ritchie fail(s) to expressly recite wherein time information includes the time of day or day of the week.
The relevance of Bradley is described above with relation to claim 3. Regarding claim 10, Bradley teaches wherein time information includes the time of day or day of the week (“locale data includes, but is not limited to, language, numerals, currency, unit of measurements, time, holiday events, weather, etc.” where “time, holiday events, [and] weather” necessarily include both time of day and day of the week.; Bradley, ¶ [0027]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the natural language processing systems of Ritchie to incorporate the teachings of Bradley to include wherein time information includes the time of day or day of the week. The device described in Bradley automatically adapts based on “locale data” including “language, numerals, currency, unit of measurements, time, holiday events, weather, etc.,” such that the “the device can be dynamically transitioned into a different locale without requiring the user to manually manipulate a specific UI setting,” which provides the known benefit of increased user convenience and improved data personalization, as recognized by Bradley. (Bradley, ¶ [0015]-[0016]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blohm (U.S. Pat. App. Pub. No. 2024/0346255) discloses systems for enhancing knowledge base (KB) operations through knowledge summarization and curation by a large language model (LLM).
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/Sean E Serraguard/Patent Examiner, Art Unit 2657