DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 6-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2025/0136130 to Shetty et al. (“Shetty”).
Regarding claim 1, Shetty discloses a computer-implemented method for vehicular management comprising:
obtaining one or more images of a vehicle occupant, using one or more imaging devices within the vehicle, wherein the one or more images include facial data of the vehicle occupant (Fig. 2; Fig. 8, element 802; paragraphs 73-78 and 137; wherein a face image of the operator/driver is captured using an interior camera of a vehicle);
analyzing, using a computing device, the one or more images to determine an in-cabin state (Fig. 2; Fig. 8, element 804; paragraphs 73-78 and 137-138; wherein the face image of an operator/driver is analyzed for alertness (i.e. “in-cabin state”));
processing the in-cabin state using a text translation layer, wherein the processing outputs one or more text sentences describing the in-cabin state (Fig. 2; Fig. 8, element 804; paragraphs 73-78 and 137-138; wherein based on the alertness (i.e. in-cabin state) a natural language sentence is processed using a look-up table (i.e. text translation layer) to provide an output of an associated natural language sentence, such as “the user has an alertness level of 4”);
seeding a generative artificial intelligence (Al) facility, using the one or more sentences (Fig. 3; Fig. 8, element 806; paragraphs 79, 85, 103 and 139, wherein the LLM (i.e. generative AI) receives (i.e. seeded with) the natural language sentence regarding alertness as input and LLM process the sentence to output a response sentence); and
managing the vehicle, based on a textual response from the generative Al facility (Fig. 8, element 808; paragraphs 103, 104, 140, wherein the vehicle is managed by presenting the output response audibly or visually, and/or by manipulating the vehicle by, for example, honking the horn in response).
Regarding claim 2, Shetty discloses the method of claim 1 wherein the text translation layer enables integration with diverse generative Al facilities (statement of intended result regarding the “text translation layer” in the step of processing, and not afforded patentable weight, see MPEP 2111.04, therefore claim 2 is considered to be anticipated by Shetty. Additionally, it can be said that use of the lookup table enables integration with diverse generative AI facilities such as those that generate conversation or synthetic data, see Shetty paragraph 140).
Regarding claim 3, Shetty discloses the method of claim 1 further comprising configuring the text translation layer using configurability parameters (paragraph 138, wherein the lookup table comprises classification scores mapped to a natural language sentence, where the scores and sentences corresponding to the broadest reasonable interpretation of “configurability parameters”).
Regarding claim 4, Shetty discloses the method of claim 3 wherein the configurability parameters include in-cabin state signal selection (paragraph 138, wherein an operator alertness classification score corresponds the broadest reasonable interpretation of an in-cabin signal selection configurability parameter).
Regarding claim 6, Shetty discloses the method of claim 3 wherein the configurability parameters enable consolidating the one or more text sentences prior to the seeding (statement of intended result regarding the “configurability parameters” used in the step of configuring the text translation layer, and not afforded patentable weight, see MPEP 2111.04, therefore claim 6 is considered to be anticipated by Shetty. Additionally, it can be said that use of the lookup table to map a score with a representative sentence thereby enables consolidating the alertness representation used as input/seeding to the generative AI to one text sentence, see Shetty paragraph 140).
Regarding claim 7, Shetty discloses the method of claim 1 wherein the text translation layer provides a prompt to the generative Al facility to enable the seeding (Fig. 3; Fig. 8, element 806; paragraphs 85, 103 and 139, wherein the LLM (i.e. generative AI) receives the natural language sentence regarding alertness as a prompt input, thereby by seeding the LLM process to output a response sentence).
Regarding claim 8, Shetty discloses the method of claim 7 wherein the prompt includes metadata instructions to the generative Al facility (paragraphs 139 and 124-126, wherein along with the natural language representation of operator alertness, the prompt sent to the LLM may include metadata such as an operator utterance (i.e. voice characteristics)).
Regarding claim 9, Shetty discloses the method of claim 8 wherein the metadata instructions include voice characteristics (paragraphs 139 and 124-126, wherein along with the natural language representation of operator alertness, the prompt sent to the LLM may include metadata such as an operator utterance (i.e. voice characteristics)).
Regarding claim 10, Shetty discloses the method of claim 1 wherein the managing the vehicle comprises captioning key moments within the vehicle (paragraphs 103 and 138-140, wherein the output of the LLM can be used to manage the audio of a vehicle that includes a caption of key moments in the vehicle such as the operator falling asleep (e.g. “I’ve just honked the horn because you’re falling asleep”)).
Regarding claim 11, Shetty discloses the method of claim 10 wherein the key moments include in-cabin state changes (paragraphs 103 and 138-140, wherein the output of the LLM can be used to manage the audio of a vehicle that includes a caption of key moments related to changes in the vehicle (i.e. in-cabin state changes) such as the operator falling asleep (e.g. “I’ve just honked the horn because you’re falling asleep”)).
Regarding claim 12, Shetty discloses the method of claim 1 wherein the managing the vehicle comprises indexing in-cabin state information (paragraphs 138-140, wherein managing the vehicle is ultimately based on and therefore comprises the use of the lookup table which comprises operator alertness classification scores (i.e. in-cabin state information) mapped to a natural language sentence, where mapping the two corresponds to “indexing” as claimed).
Regarding claim 13, Shetty discloses the method of claim 12 wherein the indexing enables data mining of an in-cabin state timeline (statement of intended result regarding the “indexing” used in the step of managing the vehicle, and not afforded patentable weight, see MPEP 2111.04, therefore claim 13 is considered to be anticipated by Shetty.).
Regarding claim 14, Shetty discloses the method of claim 12 wherein the indexing enables synchronization of in-cabin state changes and one or more responses from the generative Al facility (statement of intended result regarding the “indexing” used in the step of managing the vehicle, and not afforded patentable weight, see MPEP 2111.04, therefore claim 14 is considered to be anticipated by Shetty.).
Regarding claim 15, Shetty discloses the method of claim 1 wherein the managing the vehicle is enabled by a voice agent vocalizing the textual response from the generative Al facility (paragraphs 72, 103 and 140, wherein the vehicle is managed by vocalizing the textual output (e.g. “I’ve just honked the horn…”) response from the LLM (i.e. generative AI facility)).
Regarding claim 16, Shetty discloses the method of claim 15 further comprising seeding an additional generative Al facility response, based on a change of the in-cabin state subsequent to the managing (paragraphs 79, 80, 103-104, wherein an answer to a first AI facility response represents change of the in-cabin state and is used to seed an additional LLM response (i.e. generative AI facility response) such as “I’m glad you’re more alert.”).
Regarding claim 17, Shetty discloses the method of claim 1 wherein the managing the vehicle comprises providing vehicle manipulation instructions (paragraphs 103 and 140, wherein the vehicle is managed by/with a vehicle manipulation instruction such as an instruction to the vehicle control system to honk the horn (e.g. “I’ve just honked the horn…”)).
Regarding claim 18, Shetty discloses the method of claim 17 wherein the vehicle manipulation instructions are delivered audibly within the vehicle (paragraphs 72, 103 and 140, wherein the vehicle is managed by/with a vehicle manipulation instruction such as an instruction to vehicle control system to honk the horn that is also delivered audibly, by stating “I’ve just honked the horn…”).
Regarding claim 19, Shetty discloses the method of claim 18 wherein the vehicle manipulation instructions that are delivered audibly are directed to a driver of the vehicle (paragraphs 72, 103 and 140, wherein the vehicle is managed by/with a vehicle manipulation instruction such as an instruction to the vehicle to honk the horn that is also delivered audibly and directed to the driver (e.g. “I’ve just honked the horn…”)).
Regarding claim 20, Shetty discloses the method of claim 18 wherein the vehicle manipulation instructions that are delivered audibly are directed to a passenger of the vehicle (paragraphs 72, 103 and 140, wherein the vehicle is managed by/with a vehicle manipulation instruction such as an instruction to vehicle control system to honk the horn that is also delivered audibly and directed to the driving/operating passenger of the vehicle (e.g. “I’ve just honked the horn…”)).
Regarding claim 21, Shetty discloses the method of claim 17 wherein the vehicle manipulation instructions are delivered electronically to an autonomous or semi-autonomous vehicle control processor (paragraph 140, wherein the instructions are delivered to an auto or semi-auto vehicle control system/processor).
Regarding claim 22, Shetty discloses the method of claim 17 wherein the vehicle manipulation instructions trigger a continued dialogue between the text translation layer and the generative AI facility (paragraphs 79, 103, 104, wherein operator alertness evaluation is continued corresponding to a continued dialogue between the text translation layer or lookup table of sentences and the generative AI or LLM responses).
Regarding claim 23, Shetty discloses the method of claim 22 wherein the continued dialogue enables increasing or decreasing severity of additional vehicle management actions (statement of intended result regarding the “continued dialogue” triggered by the step of providing vehicle manipulation instructions, and not afforded patentable weight, see MPEP 2111.04, therefore claim 23 is considered to be anticipated by Shetty. However, Shetty paragraphs 79, 103, 104, does at least disclose decreasing severity of the words used (i.e. actions taken) by the vehicle management as a result of an increased alertness such an audible statement of (e.g. “I’m glad you’re more alert”).
Regarding claim 24, Shetty discloses a computer program product embodied in a non-transitory computer readable medium for vehicular management, the computer program product comprising code which causes one or more processors (Fig. 10C; Fig. 11; paragraphs 166 and 256-262) to perform operations of:
obtaining one or more images of a vehicle occupant, using one or more imaging devices within the vehicle, wherein the one or more images include facial data of the vehicle occupant (Fig. 2; Fig. 8, element 802; paragraphs 73-78 and 137; wherein a face image of the operator/driver is captured using an interior camera of a vehicle);
analyzing, using a computing device, the one or more images to determine an in-cabin state (Fig. 2; Fig. 8, element 804; paragraphs 73-78 and 137-138; wherein the face image of an operator/driver is analyzed for alertness (i.e. “in-cabin state”));
processing the in-cabin state using a text translation layer, wherein the processing outputs one or more text sentences describing the in-cabin state (Fig. 2; Fig. 8, element 804; paragraphs 73-78 and 137-138; wherein based on the alertness (i.e. in-cabin state) a natural language sentence is processed using a look-up table (i.e. text translation layer) to provide an output of an associated natural language sentence, such as “the user has an alertness level of 4”);
seeding a generative artificial intelligence (AI) facility, using the one or more sentences (Fig. 3; Fig. 8, element 806; paragraphs 79, 85, 103 and 139, wherein the LLM (i.e. generative AI) receives (i.e. seeded with) the natural language sentence regarding alertness as input and LLM process the sentence to output a response sentence); and
managing the vehicle, based on a textual response from the generative AI facility (Fig. 8, element 808; paragraphs 103, 104, 140, wherein the vehicle is managed by presenting the output response audibly or visually, and/or by manipulating the vehicle by, for example, honking the horn in response).
Regarding claim 25, Shetty discloses a computer system for vehicular management comprising:
a memory which stores instructions (Fig. 10C; Fig. 11; paragraphs 166 and 256-262);
one or more processors coupled to the memory wherein the one or more processors, when executing the instructions which are stored (Fig. 10C; Fig. 11; paragraphs 166 and 256-262), are configured to:
obtain one or more images of a vehicle occupant, using one or more imaging devices within the vehicle, wherein the one or more images include facial data of the vehicle occupant (Fig. 2; Fig. 8, element 802; paragraphs 73-78 and 137; wherein a face image of the operator/driver is captured using an interior camera of a vehicle);
analyze, using a computing device, the one or more images to determine an in- cabin state (Fig. 2; Fig. 8, element 804; paragraphs 73-78 and 137-138; wherein the face image of an operator/driver is analyzed for alertness (i.e. “in-cabin state”));
process the in-cabin state using a text translation layer, wherein the processing outputs one or more text sentences describing the in-cabin state (Fig. 2; Fig. 8, element 804; paragraphs 73-78 and 137-138; wherein based on the alertness (i.e. in-cabin state) a natural language sentence is processed using a look-up table (i.e. text translation layer) to provide an output of an associated natural language sentence, such as “the user has an alertness level of 4”);
seed a generative artificial intelligence (AI) facility, using the one or more sentences (Fig. 3; Fig. 8, element 806; paragraphs 79, 85, 103 and 139, wherein the LLM (i.e. generative AI) receives (i.e. seeded with) the natural language sentence regarding alertness as input and LLM process the sentence to output a response sentence); and
manage the vehicle, based on a textual response from the generative AI facility (Fig. 8, element 808; paragraphs 103, 104, 140, wherein the vehicle is managed by presenting the output response audibly or visually, and/or by manipulating the vehicle by, for example, honking the horn in response).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0136130 to Shetty et al. (“Shetty”) in view of US 2009/0083034 to Hernandez et al. (“Hernandez”).
Regarding claim 5, Shetty discloses the method of claim 3.
Shetty does not disclose expressly wherein the configurability parameters include a verbosity setting for the one or more text sentences.
Hernandez discloses a similar process of voice-activated vehicle control that includes configurable parameters related to urgency including a verbosity setting for natural language text sentence responses (paragraphs 70-80).
Shetty & Hernandez are combinable because they are from the same art of outputting natural language text sentences.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the technique of including a configurability parameter that includes a verbosity setting for one or more text sentences, as taught by Hernandez, into the process for configuring a lookup table of responses (i.e. text translation layer) as disclosed by Shetty.
The suggestion/motivation for doing so would have been to provide variable responses dependent on urgency (Hernandez, paragraph 70).
Therefore, it would have been obvious to combine Hernandez with Shetty to obtain the invention as specified in claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARON W CARTER whose telephone number is (571)272-7445. The examiner can normally be reached 8am - 5pm (Mon - Fri).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AARON W CARTER/Primary Examiner, Art Unit 2661
