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 .
Acknowledgement
Acknowledgement is made of applicant’s amendment made on 2/06/2026. Applicant’s submission filed has been entered and made of record.
Status of the Claims
Claims 1-9, 12-15, and 18-20 are pending.
Response to Applicant’s Arguments
In response to “The Examiner appears to equate any data selection in Sawhney with the claimed knowledge bases, now citing to Col. 25, Rows 20-30. See Office Action, page 5. Previously the Examiner had cited to Col. 4, Rows 30-36 in the Final Office Action, and prior to that, the Examiner had cited to Cols. 5, 19 and 24. See Non-Final Office Action, dated March 13, 2025, page 5. The Examiner, in yet another take on Sawhney, attempts to explain that step 2106 in Sawhney "teaches establishing a hierarchy of knowledge bases/data sources, each knowledge base ranked with the hierarchy with a level of trustworthiness associated with the knowledge base." Office Action, page 4. This simply is not a true statement. Sawhney is completely silent to an assigned hierarchy, and does not select among sources, but rather dynamically verifies them” and “Further, and to reiterate, a disclosure of multiple data sources in Sawhney does not lead to a teaching of "an assigned hierarchy of knowledge bases," within the context of the claims. Further, determining accuracy of a data source in real time does not lead to a teaching of "each knowledge base ranked within the hierarchy with a level of trustworthiness associated with the knowledge base, the knowledge base selected as a source for information regarding the user spoken utterance based on the semantic analysis," within the context of the claims”.
According to Merriam-Webster, hierarchy is defined as a graded or ranked series.1
The specification US 2024/0265916 A1 at ¶47 discloses an example of the hierarchy: “For example, if the user asks how to park, the controller may search the knowledge bases 212 for references to parking. The controller may do this based on a level of trustworthiness or hierarchy of the knowledge bases 212. For example, the controller may first search through the vehicle manual, as it is one of the more trusted sources of information based on the present hierarchy. Should the controller not find relevant data in the vehicle manual, the controller may then look to the next knowledge base 212, such as an FAQ forum”.
In the above example, it appeared that applicant’s hierarchy defined or assigned vehicle manual as the most trusted (i.e., a first level of trustworthiness) and assigned FAQ forum as the next trusted (i.e., a second level of trustworthiness).
Similarly, according to Fig. 21 of Sawhney:
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Sawhney teaches when performing semantic analysis on user spoken utterance and selecting a knowledge base from a plurality of knowledge bases as a source for information regarding the user spoken utterance based on semantic analysis (Col 4, Rows 50-60, processing natural language query for semantic information (Fig. 21, step 2106), searching for concepts using the semantic information (Fig. 21, step 2112), and formulating a response based on relevant concepts found in the search (Fig. 21, step 2114), the concepts were extracted from multimodal data corresponding to one or more data sources or “knowledge bases”), an alignment module (Col 19, Rows 10-15) determines an accuracy level of groups of related concepts based on a level of alignment among the related concepts (Col 4, Rows 60-65; see Fig. 21, step 2108) in order to establish a trustworthiness level to the one or more knowledge bases (i.e., data sources) from which the concepts were extracted (Col 24, Rows 41-44).
In one particular example where alignment module 1004 reviewed a post 1200 on a social media site stating “Crowd of protestors outside the White House” (Col 21, Rows 50-52), the alignment module 1004 accessed images or videos around the White House and retrieved image 1202 (Col 21, Rows 53-56). When the alignment module analyzed the image 1202 and determined that merely a few people gathered outside of the White House (Col 22, Rows 23-25), the alignment module 1004 assigned a tag 1204 “#sensational” to the post 1200 (Col 22, Rows 26-27), the tag described the accuracy of the contents of the post (Col 22, Rows 30-32).
By calculating (i.e., assigning) a trustworthiness level for various knowledge bases / data sources (Col 19, Rows 19-25; see also Col 24, Rows 41-44), Sawhney teaches an assigned hierarchy of trustworthy knowledge bases / data sources, each knowledge base ranked within the hierarchy with a level of trustworthiness associated with the knowledge base.
Put simply, much like the hierarchy of the specification with vehicle manual assigned with a first level of trustworthiness and the FAQ forum assigned with a second level of trustworthiness, Sawhney teaches an assigned hierarchy of data sources / knowledge bases with respective level (i.e., rank) of assigned trustworthiness.
The only difference between the specification’s hierarchy and the hierarchy of Sawhney is that the assignment of trustworthiness in Sawhney’s hierarchy is dynamic and ongoing (Col 5, Rows 1-5) whereas the assignment in the specification’s hierarchy was predetermined.
To that extent, US 2025/0327666 A1 discloses an assigned hierarchy of knowledge bases (¶10, various data sources having degrees of trustworthiness that are ascertained for respective ascertained environmental scenario; e.g., lidar data in the event of snowfall or rain may be less trustworthy than camera data or map data in the same environment scenario), where each knowledge base has a predetermined rank within the hierarchy with a level of trustworthiness associated with the knowledge base (¶38, the degrees of trustworthiness of various data sources have been specified for various environmental scenarios).
In response to “Dependent claim 3 recites in part "wherein the knowledge bases ranked higher in trustworthiness in the knowledge base hierarchy are preferred to be used as the source for information with a manufacturer supplied knowledge based ranked highest." Sawhney is silent as to any hierarchy based on a source or one where "a manufacturer supplied knowledge based [is] ranked highest." Accordingly, claim 3 is patentable for at least this additional reason”.
While Sawhney is silent regarding a manufacturer supplied knowledge base being ranked highest, Aijaz suggests vehicle manufacturer has the highest degree of trustworthiness in a hierarchy of external communication partners (¶39).
Therefore, within the context of Sawhney’s independent, distributed data sources (Col 5, Rows 33-36) as external communication partners acting as sources or bases of knowledge, it would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to have a manufacturer supplied knowledge base being ranked highest because vehicle manufacturer has the highest degree of trustworthiness as external communication partner (Aijaz, ¶39).
In response to “Dependent claim 4 recites in part "where the processor is programmed to determine whether the knowledge base ranked highest in the hierarchy includes information regarding the user spoken utterance." The portions of Sawhney cited to in rejecting this claim simply deal with "trustworthiness" and nothing to do with to the knowledge base ranked highest in the hierarchy includes information regarding the user spoken utterance," within the context of the claims. In the Office Action, the Examiner appears to continue to focus on trustworthiness, and fails to explain how such disclosure relates to the specific recitations of claim 4. For at least this additional reason, Sawhney cannot anticipate the recitations of claim 4”.
Sawhney teaches calculating a trustworthiness of a data source as an ongoing process (Col 5, Rows 4-6 and Col 24, Rows 40-44). Therefore, for data sources DS1, DS2, …., DSN (Col 5, Rows 33-36), Sawhney teaches assigning a respective trustworthiness level to DS1, DS2, …., DSN on an ongoing basis in a hierarchy that ranks from the data source with the highest level of trustworthiness to the data source with the lowest level of trustworthiness (e.g., Fig. 21 step 2108):
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Based on the established hierarchy of trustworthiness, Sawhney teaches extracting concepts from the data sources (Col 4, Rows 50-53), which includes the data source with the highest trustworthiness (i.e., knowledge base ranked highest), and search the concepts using semantic information from the user’s natural language query to formulate a response relevant to the query based on relevant concepts found in the search (Col 4, Rows 56-60).
In a particular scenario, one of the data source / knowledge base includes user submitted multimodal data as part of user’s natural language query (Col 5, Rows 5-7), which corresponds to user spoken utterance. Therefore, the ongoing determination of a trustworthiness of said user submitted multimodal data (Col 5, Rows 3-5) requires Sawhney to determine whether user submitted multimodal data / user spoken utterance is a knowledge base ranked highest in the hierarchy by calculating or assigning a level of trustworthiness to user submitted multimodal data that was part of user’s natural language query.
In response to “First, Sawhney does not disclose ranking knowledge bases for selection at all, as explained above. Second, the rejection appears to hypothesize how and if certain data sources would be ranked in Sawhney. Sawhney fails to disclose any form of ranking or a knowledge base that is "next highest ranked in the knowledge base hierarchy," within the context of the claims. Simply guessing that "any [data source] may be ranked the next highest," is unsupported by the disclosure of Sawhney. Further, Sawhney is silent as to any determination of "lacking the information regarding the user spoken utterance." Sawhney then fails to determine a "next highest" ranked knowledge base” and “Claim 5 clearly requires "a second knowledge base ranked a next highest" which is evaluated "in response to the knowledge base with the highest ranking lacking the information regarding the user spoken utterance." The Examiner's attempt to stretch the "predictable variations" of Sawhney are exceptional and even if such variations could be contemplated, still would not reach an anticipatory level disclosure necessary to reject claim 5. Accordingly, Sawhney cannot be used to render obvious the recitation of claim 5, and the Office has not met its burden with respect to the rejection of the same”.
Sawhney establishes a hierarchy of knowledge bases or data sources ranked from the highest trustworthiness level to the lowest trustworthiness level whenever Sawhney establishes a trustworthiness level to the one or more data sources (Col 24, Rows 40-44) on an ongoing basis (Col 5, Rows 3-5):
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Based on the established / assigned hierarchy of trustworthiness levels to respective data sources / knowledge bases at step 2108, Sawhney teaches extracting concepts from the data sources / knowledge bases (Col 4, Rows 50-53) and searching the concepts using semantic information from the user’s natural language query to formulate a response relevant to the query based on relevant concepts found in the search (Col 4, Rows 56-60) at step 2112.
Put simply, a first predictable result is where either (1) a data source / knowledge base contains a relevant concept or (2) the data source / knowledge base does not contain the relevant concept.
Furthermore, given data sources / knowledge bases DS1, DS2, DSN (Col 5, Rows 40-46), a second predictable result being one of the DS1-DSN would have the highest trustworthiness level and another of DS1-DSN would have the second highest trustworthiness level based on the ongoing trustworthiness level calculations of various data sources (Col 5, Rows 3-5 and Col 24, Rows 40-44).
Based on the first predictable result and the second predictable result, if Sawhney found a relevant concept to respond to the user query (Col 4, Rows 59-60), then the final predictable result is the relevant concept was either found in the data source / knowledge base that was ranked highest, data source / knowledge base ranked second highest, or data source / knowledge base ranked least trustworthy.
By logical process of elimination, if the relevant concept was not found in the data source / knowledge base ranked with the highest level of trustworthiness, then the relevant concept must be found in either the data source / knowledge base with the next highest level of trustworthiness or data source / knowledge base with the least level of trustworthiness.
Therefore, “wherein the processor is programmed to determine whether a second knowledge base ranked a next highest in the knowledge base hierarchy includes information regarding the user spoken utterance in response to the knowledge base with the highest ranking lacking the information regarding the user spoken utterance” is simply a predictable variation of the Sawhney teaching of finding a relevant concept from multimodal data extracted from one or more data sources / knowledge bases to respond to the user’s natural language query (Col 4, Rows 50-52 and Rows 57-60), the data sources / knowledge bases were assigned respective trustworthiness level to form an assigned hierarchy on an ongoing basis (Col 5, Rows 3-5 and Col 24, Rows 40-44).
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, and 20 are rejected under 35 USC 103(a) as being unpatentable over Sawhney et al. (US 10068024 B2) in view of Kalppstein et al. (US 2025/0327666 A1) and Mozer (US 8645143 B2).
Regarding Claim 1, Sawhney discloses a contextual answering system (Fig. 10 and Col 14, Rows 63-64) for processing a user spoken utterance (Col 20, Rows 39-41; see also Fig. 21, step 2106) and providing a response to the user spoken utterance (Col 20, Rows 54-57; Fig. 21, step 2114), comprising:
a vehicle head unit configured to receive microphone signals indicative of a user utterance (Col 14, Rows 63-66, apparatus 1000 is used as a question and answer system comprising a mobile interface / telephone interface; see Fig. 10 and Col 17, Rows 39-42, receiving natural language query 1005 via the telephone / mobile interface); and
a processor (Fig. 4, processor 402) programed to
receive data indicative of a vehicle state (Col 20, Rows 41-43, input data 1005 includes information regarding a user’s location available through the user’s vehicle GPS signal),
receive the user spoken utterance (Fig. 21, step 2104, receiving user query; e.g., Col 20, Rows 39-41, input data 1005 includes first question “I am about to leave work. Is Alexander Road open?”),
perform semantic analysis on the user spoken utterance based at least in part on a context of the user spoken utterance and vehicle state (Fig. 21, step 2106, processing query for semantic information; e.g., Col 20, Rows 44-53 in view of Col 4, Rows 56-59, processing the query for semantic information by accessing an extraction and indexing module 1002 to determine, via semantic and feature indexing, if there is local data available for the user’s neighborhood and for the location of the area in question; e.g., using user’s home address (context) and vehicle GPS state to process “I am about to leave work. Is Alexander Road open?”),
select a knowledge base from an assigned hierarchy of knowledge bases (Col 4, Rows 50-60, searching for concepts using the semantic information, and formulating a response based on relevant concepts found in the search, the concepts were extracted from multimodal data corresponding to one or more data sources or “knowledge bases”; i.e., select data source / knowledge base corresponding to the relevant concepts found; per Col 4, Rows 60-65, Fig. 21, step 2108, Col 19, Rows 10-15, and Col 24, Rows 41-44, alignment module determines an accuracy level of groups of related concepts in order to establish a trustworthiness level to the one or more data sources from which the concepts were extracted; i.e., alignment module establishes a hierarchy of data sources with respective assigned / calculated trustworthiness level at step 2108; for example, select data sources / knowledge bases with local data corresponding to user’s neighborhood (“work”) and location of the area in question (“Alexander road”)), each knowledge base ranked within the hierarchy with a level of trustworthiness associated with the knowledge base (Fig. 21, Col 19, Rows 19-23 and Col 24, Rows 40-44, step 2106 processing query for semantic information requires step 2108 to determine the accuracy of various related concepts to establish a trustworthiness level to the one or more data sources from which the concepts were extracted), the knowledge base selected as a source for information regarding the user spoken utterance based on the semantic analysis (Col 20, Rows 45-57, for “I am about to leave work. Is Alexander Road Open?”, respond with “I just saw a tweet that Alexander Rd. is beginning to flood”; i.e., select knowledge base / data source Tweeter to obtain information “Alexander Rd. is beginning to flood”);
determining whether the knowledge base includes information regarding the user spoken utterance (Col 20, Rows 45-57, for “I am about to leave work. Is Alexander Road Open?”, determine, via semantic and feature indexing, if there is local data available for the user’s neighborhood and for the location of the area in question; in view of Col 4, Rows 50-60, using semantic information of the query to search concepts to find relevant concepts, the concepts were extracted from multimodal data from data sources (i.e., “knowledge bases”)); and
provide, in response to the information being included in the knowledge base, a response to the user spoken utterance from the selected knowledge base to the vehicle head unit (Col 20, Rows 54-57, “I just saw a tweet that Alexander Rd. is beginning to flood” via informal gleaned from indexed data; per Col 5, Rows 5-18, determined that knowledge base (i.e., Tweeter) corresponding to the tweet that “Alexander Rd is beginning to flood” is accurate / trustworthy knowledge base, the tweet is a relevant concept and therefore the basis for the response).
If the limitation “an assigned hierarchy of knowledge bases” means “a predetermined hierarchy of knowledge bases”,2 then Sawhney does not teach predetermined hierarchy of knowledge bases because the hierarchy was calculated on an ongoing basis (Col 5, Rows 3-5).
Klappstein discloses a predetermined hierarchy of knowledge bases for a vehicle (Abstract), each knowledge base ranked within the predetermined hierarchy with respective assigned level of trustworthiness associated with respective knowledge bases (¶10, data sources with differing degrees of trustworthiness in differing environmental scenarios; e.g., lidar data in the event of snowfall or rain being less trustworthy than camera data or map data).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement a predetermined hierarchy of knowledge bases with assigned level of trustworthiness for respective knowledge bases specified for various environment scenarios while dynamically assigning degrees of trustworthiness in other instances (e.g., like the assigned hierarchy of data sources of Sawhney calculated on an ongoing basis per Col 5, Rows 3-5) in order to improve driving path estimates (Klappstein, ¶38).
Sawhney does not disclose data indicative of a vehicle state is a vehicle drive mode.
Mozer discloses performing semantic analysis on user spoken utterance based on (1) a context of the user spoken utterance and (2) vehicle state being a vehicle drive mode (Col 7, Rows 13-25, as user is moving down street 402 in his car (i.e., vehicle state being a drive mode) and travels passed a side street 406 (user context), user uttered “find gas station”, recognize that “find gas station” as destination information class “gas station” (per Col 3, Rows 59-65) and recognize the request to find a convenient gas station within a predetermined area accessed based on the direction of movement and position).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to perform semantic analysis on user spoken utterance based on (1) a context of the user spoken utterance and (2) vehicle state being a vehicle drive mode in order to improve speech recognition (Mozer, Abstract).
Regarding Claim 2, Sawhney discloses wherein each level of trustworthiness is associated with a source type of the knowledge base and not on the user spoken utterance (Col 24, Rows 41-44, at step 2106, the accuracy of various related concepts are determined, establishing a trustworthiness level to the one or more data sources from which the concepts were extracted; e.g., Col 19, Rows 32-41, when textual description of a protest clashes with image / video captured of the event, the alignment module 1004 analyzes the objects, scenes, locations, actions, events and emotions from the textual description and then scans for non-textual data to determine the level of alignment and accuracy between these data sources).
Regarding Claims 8 and 20, Sawhney does not disclose wherein the vehicle state is one of a driving state and a parked state to perform semantic analysis based on such vehicle state.
Mozer discloses a vehicle speech recognition system using vehicle state such as a driving state or parked state to perform semantic analysis based on such vehicle state (Col 2, Rows 55-65, receiving signals from satellites to generate location parameters such as position, velocity (i.e., speed and direction of travel) in order to retrieve location data; Col 3, Rows 18-23, use the location information to configure one or more recognition sets in a speech recognizer 107; Col 3, Row 58 – Col 4, Row 2, speech recognizer interprets “find gas station” to recognize the class “gas station” as destination information in order to use (1) the location information (i.e., driving state such as velocity) and (2) the destination information to find the best route from the current position to the nearest gas station; i.e., using location parameters / vehicle driving state to interpret “find gas station” as find nearest gas station).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to provide vehicle state being one of a driving state and a parked state to perform semantic analysis in order to improve speech recognition (Mozer, Abstract).
Claims 3-5 are rejected under 35 USC 103(a) as being unpatentable over Sawhney et al. (US 10068024 B2), Kalppstein et al. (US 2025/0327666 A1), and Mozer (US 8645143 B2) as applied to claim 2, in view of Aijaz (US 2009/0212928 A1).
Regarding Claim 3, Sawhney discloses wherein the knowledge bases ranked higher in trustworthiness in the knowledge base hierarchy are preferred to be used as the source for information (Col 24, Rows 40-44, establishing a trustworthiness level to the one or more data sources; per Col 19, Rows 19-23 and Rows 40-47, generating a multimodal alignment and an aggregation (i.e., hierarchy) of the accuracy of media coming from a particular news source with alignment score indicating which data is probably incorrect or the like; i.e., data source with alignment score indicating data is correct is preferred over data source with alignment score indicating data is probably incorrect).
Sawhney does not disclose with a manufacturer supplied knowledge based ranked highest.
Aijaz teaches a communication system for secure communication of vehicle with an external communication partner for the exchange of data (Abstract; e.g., ¶3, download of multimedia information for a vehicle infotainment module) and that vehicle manufacturers have the highest degree of trustworthiness amongst trustworthiness of eternal interlocutor for data exchange (¶39).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to set a manufacturer supplied knowledge base as the preferred source for information with the highest rank in trustworthiness in order to provide for secure communication of a vehicle with at least one external communication partner (Aijaz, ¶15)
Regarding Claim 4, Sawhney discloses where the processor is programmed to determine whether the knowledge base ranked highest in the hierarchy (Col 24, Rows 40-44, establishing a trustworthiness level to the one or more data sources; per Col 5, Rows 4-6, a trustworthiness of a data source is calculated as an ongoing source; i.e., determining the trustworthiness levels of data sources DS1, DS2, DS3, …DSN from highest to lowest on an ongoing basis) includes information regarding the user spoken utterance (Col 4, Rows 50-53, extract concepts from multimodal data received from one or more data sources; Col 4, Row 66 – Col 5, Row 7, multimodal data includes user submitted multimodal data (i.e., user spoken utterance is a data source) and determine the accuracy of user submitted multimodal data to determine aggregate accuracy level of related concepts from each data source such as user spoken utterance to measure the trustworthiness of the user as a data source).
Regarding Claim 5, Sawhney discloses wherein the processor is programmed to determine whether a second knowledge base with a second ranking in the knowledge base hierarchy includes information regarding the user spoken utterance in response to the knowledge base with a first ranking lacking the information regarding the user spoken utterance (Col 5, Rows 3-5, multimodal data is retrieved as a background process and a trustworthiness of a data source is calculated as an ongoing process; e.g., Col 7, Rows 13-25 and Rows 45-66, if user queries or is interested in “protest on Smith St.”, a correlation module correlates textual news report regarding the formation of a protested extracted from DS1, video data of the protest extracted from DS2, and audio clips from analysts discussing the protest extracted from DS3 as part of the background process while respective trustworthiness of sources DS1, DS2, and DS3 are calculated as an ongoing process; in this situation, data regarding “protest on Smith St.” are collected from DS1, DS2, and DS3 because such data is lacking from sources such as vehicle sensor data per Col 5, Rows 12-15).
Sawhney does not teach that the knowledge base with a first ranking lacking the information regarding the user spoken utterance is the knowledge base with the highest ranking.
However, the teaching to calculate respective trustworthiness of the list of knowledge bases as an ongoing process would yield three predictable variations:
The trustworthiness of the knowledge base with the first ranking (i.e., vehicle sensor data) is ranked the highest as part of the ongoing calculation.
The trustworthiness of the knowledge base with the first ranking (i.e., vehicle sensor data) is ranked the lowest as part of the ongoing calculation.
The trustworthiness of the knowledge base with the first ranking (i.e., vehicle sensor data) is ranked somewhere in between the lowest and the highest as part of the ongoing calculation.
Therefore, it would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention that predictable variation (1) in Sawhney discloses where the knowledge base with the first ranking is the highest ranked but lacking information regarding the user spoken utterance (e.g., the vehicle sensor does not have data regarding “protest on Smith St.”). In such scenario. the system would collect information regarding the user spoken utterance “protest on Smith St.” from knowledge bases DS1 (“news report”), DS2 (“video source”), and DS3 (“audio source”), any of which may be ranked the next highest as part of the ongoing process to calculate the trustworthiness of respective data source (Sawhney, Col 5, Rows 3-5) in order to formulate a response to the query based on relevant concepts found in the search of the respective data sources (Sawhney, Col 4, Rows 56-60).
Claims 6-7 are rejected under 35 USC 103(a) as being unpatentable over Claims 3-4 are rejected under 35 USC 103(a) as being unpatentable over Sawhney et al. (US 10068024 B2), Kalppstein et al. (US 2025/0327666 A1), Mozer (US 8645143 B2), and Aijaz (US 2009/0212928 A1) as applied to claim 4, in further view of Garcia et al. (US 2020/0216089 A1).
Regarding Claim 6, Sawhney does not teach wherein the knowledge base ranked the highest by the knowledge base hierarchy is supplied by a manufacture of the vehicle.
Garcia teaches an AI engine on a vehicle for processing queries and dynamically determining when and how to display resulting content (¶30) by obtaining content from data source installed in a vehicle’s storage device (¶31) that is supplied by a manufacture of the vehicle (¶32, all vehicle system related information including documentation, warranty information, user guides, emergency or maintenance contact information, etc.).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to provide a knowledge base supplied by the manufacturer of the vehicle in order to provide contents relating to queries on vehicle operation (Garcia ¶32).
According to Aijaz, such manufacturer supplied knowledge base would rank the highest because vehicle manufacturers have the highest degree of trustworthiness as external party for exchange of data / knowledge base (Aijaz ¶39).
Regarding Claim 7, Sawhney discloses wherein the processor is programed to instruct the head unit to audibly or visually present the response (Col 4, Rows 36-47, present a common operating picture as output comprising textual, visual, or audio answers).
Claims 9, 12-15, and 18-19 are rejected under 35 USC 103(a) as being unpatentable over Sawhney et al. (US 10068024 B2) and Kalppstein et al. (US 2025/0327666 A1).
Regarding Claim 9, Sawhney discloses a method for processing a user spoken utterance and providing a response to the user spoken utterance (Fig. 21), comprising:
receiving the user spoken utterance (Fig. 21, step 2104, receiving user query; e.g., Col 20, Rows 39-41, input data 1005 includes first question “I am about to leave work. Is Alexander Road open?”),
perform semantic analysis on the user spoken utterance (Col 4, Rows 50-60, processing natural language query for semantic information; see Fig. 21, step 2106, processing query for semantic information; e.g., Col 20, Rows 47-48, semantic and feature indexing),
select a knowledge base from an assigned hierarchy of knowledge bases (Col 4, Rows 50-60, searching for concepts using the semantic information, and formulating a response based on relevant concepts found in the search, the concepts were extracted from multimodal data corresponding to one or more data sources or “knowledge bases”; i.e., select data source / knowledge base corresponding to the relevant concepts found; per Col 4, Rows 60-65, Fig. 21, step 2108, Col 19, Rows 10-15, and Col 24, Rows 41-44, alignment module determines an accuracy level of groups of related concepts in order to establish a trustworthiness level to the one or more data sources from which the concepts were extracted; i.e., alignment module establishes a hierarchy of data sources with respective assigned / calculated trustworthiness level at step 2108), each knowledge base ranked within the hierarchy with a level of trustworthiness associated with the knowledge base (Fig. 21, Col 19, Rows 19-23 and Col 24, Rows 40-44, step 2106 processing query for semantic information requires step 2108 to determine the accuracy of various related concepts to establish a trustworthiness level to the one or more data sources from which the concepts were extracted), the knowledge base selected as a source for information regarding the user spoken utterance based on the semantic analysis (Col 20, Rows 45-57, for “I am about to leave work. Is Alexander Road Open?”, respond with “I just saw a tweet that Alexander Rd. is beginning to flood”; i.e., select knowledge base / data source Tweeter to obtain information “Alexander Rd. is beginning to flood”);
determining whether the knowledge base includes information regarding the user spoken utterance (Col 20, Rows 45-57, for “I am about to leave work. Is Alexander Road Open?”, determine, via semantic and feature indexing, if there is local data available for the user’s neighborhood and for the location of the area in question; in view of Col 4, Rows 50-60, using semantic information of the query to search concepts to find relevant concepts, the concepts were extracted from multimodal data from data sources (i.e., “knowledge bases”)); and
provide, in response to the information being included in the knowledge base, a response to the user spoken utterance from the selected knowledge base to the vehicle head unit (Col 20, Rows 54-57, “I just saw a tweet that Alexander Rd. is beginning to flood” via informal gleaned from indexed data; per Col 5, Rows 5-18, determined that knowledge base (i.e., Tweeter) corresponding to the tweet that “Alexander Rd is beginning to flood” is accurate / trustworthy knowledge base, the tweet is a relevant concept and therefore the basis for the response).
If the limitation “an assigned hierarchy of knowledge bases” means “a predetermined hierarchy of knowledge bases”,3 then Sawhney does not teach predetermined hierarchy of knowledge bases because the hierarchy was calculated on an ongoing basis (Col 5, Rows 3-5).
Klappstein discloses a predetermined hierarchy of knowledge bases for a vehicle (Abstract), each knowledge base ranked within the predetermined hierarchy with respective assigned level of trustworthiness associated with respective knowledge bases (¶10, data sources with differing degrees of trustworthiness in differing environmental scenarios; e.g., lidar data in the event of snowfall or rain being less trustworthy than camera data or map data).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement a predetermined hierarchy of knowledge bases with assigned level of trustworthiness for respective knowledge bases specified for various environment scenarios while dynamically assigning degrees of trustworthiness in other instances (e.g., like the assigned hierarchy of data sources of Sawhney calculated on an ongoing basis per Col 5, Rows 3-5) in order to improve driving path estimates (Klappstein, ¶38).
Regarding Claim 12, Sawhney discloses where the processor is programmed to determine whether the knowledge base ranked highest in the hierarchy includes information regarding the user spoken utterance (Col 24, Rows 40-44, establishing a trustworthiness level to the one or more data sources; per Col 5, Rows 4-5, a trustworthiness of a data source is calculated as an ongoing basis; i.e., determining the trustworthiness levels of data sources DS1, DS2, DS3, …DSN from highest (per Col 19, Rows 10-13, data source with data accurately describing an event or an ongoing situation) to lowest (per Col 19, Rows 44-47, data source with data that are out of sync, that mischaracterized image or text, or data is probably incorrect) on an ongoing basis; per Col 4, Row 66 – Col 5, Row 7, because multimodal data includes user submitted multimodal data, user spoken utterance information is also a data source such that ongoing calculation of trustworthiness of data sources requires alignment module to determine whether user spoken utterance information is a data source would have the highest trustworthiness level).
Regarding Claim 14, Sawhney discloses instructing the user device to audibly or visually present the response (Col 4, Rows 36-47, present a common operating picture as output comprising textual, visual, or audio answers).
Regarding Claim 15, Sawhney discloses a contextual answering system (Fig. 10 and Col 14, Rows 63-64) for processing a user spoken utterance (Col 20, Rows 39-41; see also Fig. 21, step 2106) and providing a response to the user spoken utterance (Col 20, Rows 54-57; see also Fig. 21, step 2114), comprising:
a vehicle head unit configured to receive microphone signals indicative of a user utterance (Col 14, Rows 63-66, apparatus 1000 is used as a question and answer system comprising a mobile interface / telephone interface; see Fig. 10 and Col 17, Rows 39-42, receiving natural language query 1005 via the telephone / mobile interface); and
a processor (Fig. 4, processor 402) programed to
receive the user spoken utterance (Fig. 21, step 2104, receiving user query; e.g., Col 20, Rows 39-41, input data 1005 includes first question “I am about to leave work. Is Alexander Road open?”),
perform semantic analysis on the user spoken utterance (Col 4, Rows 50-60, processing natural language query for semantic information; see Fig. 21, step 2106, processing query for semantic information; e.g., Col 20, Rows 47-48, semantic and feature indexing),
select a knowledge base from an assigned hierarchy of knowledge bases (Col 4, Rows 50-60, searching for concepts using the semantic information, and formulating a response based on relevant concepts found in the search, the concepts were extracted from multimodal data corresponding to one or more data sources or “knowledge bases”; i.e., select data source / knowledge base corresponding to the relevant concepts found; per Col 4, Rows 60-65, Fig. 21, step 2108, Col 19, Rows 10-15, and Col 24, Rows 41-44, alignment module determines an accuracy level of groups of related concepts in order to establish a trustworthiness level to the one or more data sources from which the concepts were extracted; i.e., alignment module establishes a hierarchy of data sources with respective assigned / calculated trustworthiness level at step 2108 of Fig. 21), each knowledge base ranked within the hierarchy with a level of trustworthiness associated with the knowledge base (Fig. 21, Col 19, Rows 19-23 and Col 24, Rows 40-44, step 2106 processing query for semantic information requires step 2108 to determine the accuracy of various related concepts to establish a trustworthiness level to the one or more data sources from which the concepts were extracted), the knowledge base selected as a source for information regarding the user spoken utterance based on the semantic analysis (Col 20, Rows 45-57, for “I am about to leave work. Is Alexander Road Open?”, respond with “I just saw a tweet that Alexander Rd. is beginning to flood”; i.e., select knowledge base / data source Tweeter to obtain information “Alexander Rd. is beginning to flood”);
determining whether the knowledge base includes information regarding the user spoken utterance (Col 20, Rows 45-57, for “I am about to leave work. Is Alexander Road Open?”, determine, via semantic and feature indexing, if there is local data available for the user’s neighborhood and for the location of the area in question; in view of Col 4, Rows 50-60, using semantic information of the query to search concepts to find relevant concepts, the concepts were extracted from multimodal data from data sources (i.e., “knowledge bases”)); and
provide a response to the user spoken utterance from the selected knowledge base to the vehicle head unit (Col 20, Rows 54-57, “I just saw a tweet that Alexander Rd. is beginning to flood” via informal gleaned from indexed data; per Col 5, Rows 5-18, determined that knowledge base (i.e., Tweeter) corresponding to the tweet that “Alexander Rd is beginning to flood” is accurate / trustworthy knowledge base, the tweet is a relevant concept and therefore the basis for the response).
If the limitation “an assigned hierarchy of knowledge bases” means “a predetermined hierarchy of knowledge bases”,4 then Sawhney does not teach predetermined hierarchy of knowledge bases because the hierarchy was calculated on an ongoing basis (Col 5, Rows 3-5).
Klappstein discloses a predetermined hierarchy of knowledge bases for a vehicle (Abstract), each knowledge base ranked within the predetermined hierarchy with respective assigned level of trustworthiness associated with respective knowledge bases (¶10, data sources with differing degrees of trustworthiness in differing environmental scenarios; e.g., lidar data in the event of snowfall or rain being less trustworthy than camera data or map data).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement a predetermined hierarchy of knowledge bases with assigned level of trustworthiness for respective knowledge bases specified for various environment scenarios while dynamically assigning degrees of trustworthiness in other instances (e.g., like the assigned hierarchy of data sources of Sawhney calculated on an ongoing basis per Col 5, Rows 3-5) in order to improve driving path estimates (Klappstein, ¶38).
Regarding Claim 18, Sawhney discloses where the processor is programmed to determine whether the knowledge base ranked highest in the hierarchy includes information regarding the user spoken utterance (Col 24, Rows 40-44, establishing a trustworthiness level to the one or more data sources; per Col 5, Rows 4-5, a trustworthiness of a data source is calculated as an ongoing basis; i.e., determining the trustworthiness levels of data sources DS1, DS2, DS3, …DSN from highest (per Col 19, Rows 10-13, data source with data accurately describing an event or an ongoing situation) to lowest (per Col 19, Rows 44-47, data source with data that are out of sync, that mischaracterized image or text, or data is probably incorrect) on an ongoing basis; per Col 4, Row 66 – Col 5, Row 7, because multimodal data includes user submitted multimodal data, user spoken utterance information is also a data source such that ongoing calculation of trustworthiness of data sources requires alignment module to determine whether user spoken utterance information is a data source would have the highest trustworthiness level).
Regarding Claim 19, Sawhney discloses wherein the processor is programmed to receive data indicative of a vehicle state (Col 20, Rows 41-43, input data 1005 includes information regarding a user’s location available through the user’s vehicle GPS signal) and perform semantic analysis on the user utterance based at least in part on the vehicle state (Col 44-53, access an extraction and indexing module 1002 to determine, via semantic and feature indexing, if there is local data available for the user’s neighborhood and for the location of the area in question; e.g., the location of Alexander Road is inferred based on the location of the user’s home, the user’s current location, or a location of the user’s destination).
Regarding Claim 13, Sawhney discloses wherein the processor is programmed to determine whether a second knowledge base with a second ranking in the knowledge base hierarchy includes information regarding the user spoken utterance in response to the knowledge base with a first ranking lacking the information regarding the user spoken utterance (Col 5, Rows 3-5, multimodal data is retrieved as a background process and a trustworthiness of a data source is calculated as an ongoing process; e.g., Col 7, Rows 13-25 and Rows 45-66, if user queries or is interested in “protest on Smith St.”, a correlation module correlates textual news report regarding the formation of a protested extracted from DS1, video data of the protest extracted from DS2, and audio clips from analysts discussing the protest extracted from DS3 as part of the background process while respective trustworthiness of sources DS1, DS2, and DS3 are calculated as an ongoing process; in this situation, data regarding “protest on Smith St.” are collected from DS1, DS2, and DS3 because such data is lacking from sources such as vehicle sensor data per Col 5, Rows 12-15).
Sawhney does not teach that the knowledge base with a first ranking lacking the information regarding the user spoken utterance is the knowledge base with the highest ranking.
However, the teaching to calculate respective trustworthiness of the list of knowledge bases as an ongoing process would yield three predictable variations:
The trustworthiness of the knowledge base with the first ranking (i.e., vehicle sensor data) is ranked the highest as part of the ongoing calculation.
The trustworthiness of the knowledge base with the first ranking (i.e., vehicle sensor data) is ranked the lowest as part of the ongoing calculation.
The trustworthiness of the knowledge base with the first ranking (i.e., vehicle sensor data) is ranked somewhere in between the lowest and the highest as part of the ongoing calculation.
Therefore, it would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention that predictable variation (1) in Sawhney discloses where the knowledge base with the first ranking is the highest ranked but lacking information regarding the user spoken utterance (e.g., the vehicle sensor does not have data regarding “protest on Smith St.”). In such scenario. the system would collect information regarding the user spoken utterance “protest on Smith St.” from knowledge bases DS1 (“news report”), DS2 (“video source”), and DS3 (“audio source”), any of which may be ranked the next highest as part of the ongoing process to calculate the trustworthiness of respective data source (Sawhney, Col 5, Rows 3-5) in order to formulate a response to the query based on relevant concepts found in the search of the respective data sources (Sawhney, Col 4, Rows 56-60).
Conclusion
Applicant's amendment necessitated the new grounds 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 extension fee 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 examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 04/21/2026
1 HIERARCHY Definition & Meaning - Merriam-Webster
2 The specification US 2024/0265916 A1 at ¶47 discloses an example of the hierarchy: “For example, the controller may first search through the vehicle manual, as it is one of the more trusted sources of information based on the present hierarchy. Should the controller not find relevant data in the vehicle manual, the controller may then look to the next knowledge base 212, such as an FAQ forum”.
3 The specification US 2024/0265916 A1 at ¶47 discloses an example of the hierarchy: “For example, the controller may first search through the vehicle manual, as it is one of the more trusted sources of information based on the present hierarchy. Should the controller not find relevant data in the vehicle manual, the controller may then look to the next knowledge base 212, such as an FAQ forum”.
4 The specification US 2024/0265916 A1 at ¶47 discloses an example of the hierarchy: “For example, the controller may first search through the vehicle manual, as it is one of the more trusted sources of information based on the present hierarchy. Should the controller not find relevant data in the vehicle manual, the controller may then look to the next knowledge base 212, such as an FAQ forum”.
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