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 .
Claims 1-20 are pending and examined in this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, mathematical relationship or an abstract idea) without significantly more.
Statutory Category: Claim 1 recites a method, comprising: generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of an environment, a feature vector corresponding to a tokenized description of at least the portion of the environment; performing, using the feature vector, a similarity search of a set of one or more previously-determined feature vectors to determine one or more similar feature vectors; updating the tokenized description of at least the portion of the environment based in part upon one or more additional observations obtained for at least the portion of the environment until a single similar feature vector is identified though the similarity search; and identifying a geographic location, associated with the single similar feature vector, as a current location in the environment..
Step 2A – Prong 1: Claim 1 recites: generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of an environment, a feature vector corresponding to a tokenized description of at least the portion of the environment (a mental step of creating a data structure); performing, using the feature vector, a similarity search of a set of one or more previously-determined feature vectors to determine one or more similar feature vectors (a mental step of searching for a data pattern); updating the tokenized description of at least the portion of the environment based in part upon one or more additional observations obtained for at least the portion of the environment until a single similar feature vector is identified though the similarity search (a mental step of updating data); identifying a geographic location, associated with the single similar feature vector, as a current location in the environment (a mental step of identification). That is, nothing in the claim elements precludes the steps from practically being performed mentally or using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the mental process grouping of abstract idea. Accordingly, the claim recites an abstract idea under step 2A prong 1.
Dependent claims 2-9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the dependent claims 2-9 recite more steps of a mental process (such as identifying, determining, selecting) which can be performed mentally or using pen and paper. The additional element of dependent claims 2-9 recite more extra-solution activities (data capturing/gathering, providing data input), which do not impose any meaningful limits on practicing the mental process (insignificant additional element). Therefore, these claims are not patent eligible.
Independent claim 10 (a processor with circuit to perform the method of claim 1) with dependent claims 11-15 are rejected under the similar rational as claims 1-9. The additional elements in the claim amounts to no more than generic hardware component with instructions to apply the exception, which cannot integrate a judicial exception into a practical application or provide an inventive concept.
Independent claim 16 (a system with a processor to perform the method similar to claim 1) with dependent claims 17-20 are rejected under the similar rational as claims 1-9. The additional elements in the claim amounts to no more than generic hardware component with instructions to apply the exception, which cannot integrate a judicial exception into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7, 9-11, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US PGPUB 20220335074) hereinafter Huang, in view of Yuan et al. (US patent 9424461) hereinafter Yuan.
Per claim 1, Huang discloses a method, comprising: generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of an environment, a feature vector corresponding to a tokenized description of at least the portion of the environment (claim 1; paragraphs [0061][0062][0039][0057]; a user enters the search term (observation of environment) such as “Eiffel tower”, a vector representation of each character in the search term is determined using a first neural network); performing, using the feature vector, a similarity search of a set of one or more previously-determined feature vectors to determine one or more similar feature vectors; identifying a geographic location, associated with the single similar feature vector, as a current location in the environment (paragraphs [0038][0058]; a second neural network is used to determine a vector representation of each geographic location in a map database (a set of one or more previously-determined feature vectors), a similarity between the vector representation of the search term and the vector representation of the each geographic location is calculated, and the geographic location is retrieved according to the similarity; the each geographic location may be sorted according to the similarity from high to low, and the geographic location is retrieved according to the sorted result).
Huang does not explicitly teach updating the tokenized description of at least the portion of the environment based in part upon one or more additional observations obtained for at least the portion of the environment until a single similar feature vector is identified though the similarity search. However, Yuan suggests the above (column 3, line 25-47; prompt the user for additional information (description update) as necessary to attempt to narrow search categories for improve matching results for one or more objects in the captured image information, compact combined visual feature vector can be compared to one or more stored vectors of a set of stored vectors, where each of the set of stored vectors corresponds to a respective type of object, a matching stored vector (a single similar feature vector is identified) having a respective similarity score that at least meets a matching threshold can be determined). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention combine Huang and Yuan to update environment description from observations until a single similar feature vector is identified, this would ensure the best matched/most similar result from a plurality of results can be identified, thus providing the most accurate location.
Per claim 2, Huang further suggests wherein the one or more previously-determined feature vectors correspond to a set of points in a latent space, and wherein the one or more similar feature vectors are determined for the feature vector based at least on a proximity in the latent space (paragraphs [0056][0061][0062]; the search term and the vector representation of the geographic location are obtained by using the vector representation of the each character, even if a full name “Kentucky Fried Chicken” is used in the description text, they have a very close distance in the same vector space).
Per claim 3, Yuan further suggests capturing sensor data, at an initial location of a vehicle in the environment, to be used to generate at least a subset of observations; and capturing additional sensor data over one or more subsequent positions of the vehicle to generate the additional observations (column 3, line 25-47; prompt the user for additional information as necessary to attempt to narrow search categories for improve matching results for one or more objects in the captured image information; column 2, line 1-22; capturing images from a recording device to identify an object; providing additional data including 3D image data to better identify the object; column 23, line 60-65; the recording device is in a car; thus, it would have been obvious that the first images and the additional images are captured at different positions in a moving car).
Per claim 7, Huang further suggests wherein the tokenized description includes a tokenized sequence representative of at least the portion of the environment, in which tokens are associated with objects or features, and wherein the feature vector is generated based in part on the tokenized sequence (claim 1; paragraphs [0061][0062][0039][0057]; a user enters the search term (observation of environment), a vector representation of each character (token) in the search term is determined using a first neural network; similarity may be integrated into the existing sorting model by one of the feature vectors).
Per claim 9, Huang further suggests wherein the tokenized description is determined based on at least one of semantic, topological, geometric, kinematic, or relational information of features identified from the set of observations (claim 1; paragraphs [0061][0062][0039][0057]; a user enters the search term (observation of environment), a vector representation of each character in the search term is determined using a first neural network (using semantic features)).
Per claim 15, Huang further suggests wherein the processor is comprised in at least one of: a system for performing deep learning operations (claim 1; a system that uses neural network to performs deep learning and training).
Claims 10-11 recite similar limitations as claims 1 and 3. Therefore, claims 10-11 are rejected under similar rationales as claims 1 and 3.
Per claim 16, Huang discloses a system comprising: one or more processors to determine a location of a device based in part on a tokenized description of a set of observations obtained for the location, the tokenized description to be used in a similarity search of a set of previously-generated tokenized descriptions to identify a geographic location associated with a most similar result of the similarity search. (claim 1; paragraphs [0061][0062][0039][0057]; a user enters the search term (observation of environment) such as “Eiffel tower”, a vector representation of each character in the search term is determined using a first neural network; paragraphs [0038][0058]; a second neural network is used to determine a vector representation of each geographic location in a map database, a similarity between the vector representation of the search term and the vector representation of the each geographic location is calculated, and the geographic location is retrieved according to the similarity; the each geographic location may be sorted according to the similarity from high to low, and the geographic location is retrieved according to the sorting result).
Huang does not explicitly teach the device is a vehicle. However, Yuan discloses (column 23, line 60-65; put a recording device in a car). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention combine Huang and Yuan to put in the recording device in a car, so Huang’s invention can be used to locate a vehicle’s location (increase the versatility of Huang’s invention).
Per claim 17, Yuan further suggests use additional observations obtained for the location to narrow down a set of similarity search results and identify the most similar result (column 3, line 25-47; prompt the user for additional information (description update) as necessary to attempt to narrow search categories for improve matching results for one or more objects in the captured image information, compact combined visual feature vector can be compared to one or more stored vectors of a set of stored vectors, where each of the set of stored vectors corresponds to a respective type of object, a matching stored vector (a single similar feature vector is identified) having a respective similarity score that at least meets a matching threshold can be determined).
Claim 18 is rejected under similar rationales as claim 3.
Claim 20 is rejected under similar rationales as claim 15.
Claims 4-6, 12-14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Yuan, in further view of Kundu (US PGPUB 2024/0414337).
Per claim 4, Huang does not explicitly teach providing the geographic location, the tokenized description, and a road-level route plan as input to a second language model; and receiving, as output of the second language model, a tokenized representation of routing data, including a further level of detail, to be used to follow the road-level route plan. However, Kundu suggests the above (paragraph [0137]; an analytics module may execute a routing and monitoring algorithm that accepts inputs of vehicle source location (geographic location), destination location (description), map (road-level route plan), and determines a candidate route (routing data) for the vehicle to reach the destination location; the routing and monitoring algorithm may be executed based on an AI-based model). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Huang, Yuan and Kundu to use an AI model to produce tokenized routing data to allow a vehicle to reach a destination, as the AI model can predict the shortest and fastest route.
Per claim 5, Kundu further suggests causing the second language model to identify a sequence of goals corresponding to the road-level route plan; determining a set of path options for satisfying the sequence of goals; and selecting, from the set of path options, an optimal path option to use to generate the tokenized representation of the routing data (paragraphs [0137][0114][0146]; an analytics module may execute a routing and monitoring algorithm that accepts inputs of vehicle source location, destination location, map, and determines a candidate route for the vehicle to reach the destination location; the routing and monitoring algorithm may be executed based on an AI-based model; each route may be divided into a plurality of segments based on waypoint nodes (goals); when all candidate routes have been processed, the service computing device may select an optimal route based various factors).
Per claim 6, Kundu further suggests providing the tokenized representation of the routing data as input to a control system for operating an object according to the routing data in the tokenized representation (paragraphs [0137][0098]; providing the determined optimal routing data, the vehicle can reach the destination using automated driving technique).
Claims 12-14 recite similar limitations as claims 4-6. Therefore, claims 12-14 are rejected under similar rationales as claims 4-6.
Claim 19 is rejected under similar rationales as claim 5.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Yuan, in further view of Lora et al. (US PGPUB 2019/0251759) hereinafter Lora.
Per claim 8, Huang does not explicitly teach wherein the tokenized description is written in a road topology language (RTL) or other domain specific language (DSL). However, Lora suggests the above (paragraph [0181]; using a proprietary domain specific language to write a tokenized description). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Huang, Yuan and Lora to use a proprietary domain specific language to write a tokenized description, which offer better data protection (more difficult for others to parse the description).
Conclusion
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/HANG PAN/Primary Examiner, Art Unit 2193