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 Objections
Claim 17 is objected to because of the following informalities:
Claim 17 line 1 should read:
“A method of operating a processing platform, the processing platform comprising: […]”.
Appropriate correction is required.
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)(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.
Claims 1-4, 6, 9-12, 14, and 17-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kallman et al. (US 20240419749 A1), hereinafter Kallman.
Regarding claims 1 and 9, Kallman teaches a computing apparatus and method thereof comprising:
one or more computer-readable storage media, one or more processors operatively coupled to the one or more computer-readable storage media, and program instructions stored on the one or more computer-readable storage media (para. 77, “Computer system 900 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 900 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 900 in response to processor 904 executing one or more sequences of one or more instructions contained in main memory 906. Such instructions may be read into main memory 906 from another storage medium, such as storage device 910.”) that, when executed by the one or more processors, direct the computing apparatus to:
receive a request for information based on synthetic aperture radar (SAR) data, generate a prompt for a large language model (LLM) based on the request, wherein the prompt includes a request for processing instructions for processing the SAR data, generate a processing algorithm based on the processing instructions, and process the SAR data according to the processing algorithm (paras. 39-40, “A query preprocessing module 402 receives a query from the user 102. The user query can be received in the form of a text string. The user can enter a natural language prompt to the geospatial online search system 108. In other words, the geospatial online search system 108 can receive an unstructured query. […] In some embodiments, the query preprocessing module 402 can be implemented with language models, including for example, large language models [LLMs], natural language processing [NLP] models, or a combination of one or more language models. The NLPs can be implemented with smaller models and can efficiently return query labels for a received query. When the NLPs do not return labels for a query, one or more LLMs, which can be more resource intensive and more time consuming, can be deployed.”; paras. 52-53, “For example, sensors that collect electrooptical [EO], or synthetic aperture radar [SAR] satellite imagery can be the relevant sources to the subjects of interest in a query in the field of geospatial search. The query filters 514 can be built based on encoding the relationships between the attributes of the query and the attributes and/or characteristics of the relevant sources within which the query can be searched. As an example, various models 516 can build one or more subject feature graph 518, directed to the features of potential subjects of interest in user queries.”; para. 55, “As an example, if the user query is about an object, the query filters 514 can generate filters that exclude content from sources that have sensor data whose resolutions would not be sufficient to adequately depict an image of the object of interest. Similarly, if a user query is received that indicates an interest in an object being observed at nighttime, the query filters 514 can apply to exclude EO satellite imagery of the object but include SAR satellite imagery of the object.”; an LLM processes a natural language user inputted query in order to for a model to process sensor data which includes SAR data, wherein the processing includes building a subject feature graph of the sensor data and including or excluding SAR data, wherein building a subject feature map is according to an algorithm that is generated based on the query).
Regarding claims 2 and 10, Kallman teaches the computing apparatus of claim 1 and the method of claim 9 respectively,
wherein to generate a processing algorithm based on the processing instructions, the program instructions direct the computing apparatus to identify processing operations for performing steps of the processing instructions and to encode the processing operations for the processing algorithm (para. 52, “For example, in the field of geospatial search, a user query may be to search a subject of interest in relation to sensor data and databases that contain sensor data. For example, sensors that collect electrooptical [EO], or synthetic aperture radar [SAR] satellite imagery can be the relevant sources to the subjects of interest in a query in the field of geospatial search. The query filters 514 can be built based on encoding the relationships between the attributes of the query and the attributes and/or characteristics of the relevant sources within which the query can be searched.”).
Regarding claims 3 and 11, Kallman teaches the computing apparatus of claim 2 and the method of claim 10 respectively,
wherein to encode the processing operations for the processing algorithm comprises configuring a series of SAR modules and primitives by which to process the SAR data (para. 29, “The user 102 can use a variety of computing devices 104 to access the system 108 via the Internet 106, pose a query and receive results. The user 102 can be an individual, who maintains an account with the geospatial online search system 108 or can be an employee or an agent of an organization that maintains an account with the system 108. The geospatial online search system 108 can utilize both public sources 110 and private sources 112 to prepare indexes to content that may be of interest to users 102.”; indexes correspond to integer data; para. 53, “For example, a source feature graph 522 directed to an EO satellite imagery sensor and database, among encoding other information about an EO sensor and database, can encode that the EO satellite imagery is irrelevant or less relevant to low light conditions or conditions where the imaged area is obstructed [e.g., by clouds]. Another source feature graph 522, among encoding other information about a SAR sensor and database, can encode that SAR imagery is relevant regardless of light conditions and/or obstructions.”).
Regarding claims 4, 12, and 18, Kallman teaches the computing apparatus of claim 3 and the methods of claims 11 and 17 respectively,
wherein the program instructions further direct the computing apparatus to pre-process the SAR data to standardize a format of the SAR data (para. 57, “Preprocessors 606 can be built with natural language processing [NLPs] to perform detection of geospatial data and/or perform preliminary classification on the fetched content. Preliminary classification can include generating metadata for the fetched content to label type and format of the geospatial data present in the fetched content. Furthermore, the preprocessors 606 can generate metadata related to the frequency of the information updates for the fetched data. Example formats and classifications of the fetched content at the preprocessors 606 stage can include labeling whether the fetched content includes SAC-compliant data, open geospatial consortium-compliant data [e.g., Geotopes, etc.] and how frequently the data is updated.”; generation of metadata involves the structuring of data according to an established schema).
Regarding claims 6 and 14, Kallman teaches the computing apparatus of claim 5 and the method of 13 respectively,
wherein the program instructions further direct the computing apparatus to select the SAR data for processing based on the processing instructions (para. 52, “The query filters 514 can depend on the domain, search field, and the overall attributes of the query, but also on the characteristics and attributes of the available sources of content in which the search for the query is to be performed. For example, in the field of geospatial search, a user query may be to search a subject of interest in relation to sensor data and databases that contain sensor data. For example, sensors that collect electrooptical [EO], or synthetic aperture radar [SAR] satellite imagery can be the relevant sources to the subjects of interest in a query in the field of geospatial search. The query filters 514 can be built based on encoding the relationships between the attributes of the query and the attributes and/or characteristics of the relevant sources within which the query can be searched.”).
Regarding claim 17, Kallman teaches a method of operating a processing platform, the processing platform comprising:
a prompt engine (Fig. 4, query preprocessing module 402, query understanding module 408),
a natural language processing engine (para. 40, “In some embodiments, the query preprocessing module 402 can be implemented with language models, including for example, large language models [LLMs], natural language processing [NLP]s models, or a combination of one or more language models.”), and
a data processing engine (Fig. 5, query execution module 412), the method comprising:
by the prompt engine:
receiving a request for information based on synthetic aperture radar (SAR) data (para. 39, “A query preprocessing module 402 receives a query from the user 102. The user query can be received in the form of a text string. The user can enter a natural language prompt to the geospatial online search system 108. In other words, the geospatial online search system 108 can receive an unstructured query. The query preprocessing module 402 receives the unstructured user query and generates a structured user query. Structured query can include labels and classifications of the user query. For example, the structured query can include parts-of-speech labels, classification of words labels, subject labels, topics labels, context labels, location labels, including for example, coordinates, areas or regions, timing labels, topics of interest, analytics reports of interest, entity names, address labels, etc”),
generating a prompt for a generative artificial intelligence (AI) model based on the request, wherein the prompt includes a request for instructions for processing the SAR data (para. 41, “The structured query is received by a query understanding module 408. The query understanding module 408 maps the structured query to a root query. The root query is a representation of similar queries. The geospatial online search system 108 can receive multiple semantically similar queries from one or more users 102. The root query can be used to handle and improve searching for multiple queries that can be directed to similar searches. In one embodiment, the query understanding module 408 can map the structured query to a root query using vector space embeddings for the queries. Queries having similar vector space embeddings can be mapped to a shared root query.”), and
responsive to submitting the prompt to the generative AI model, receiving the processing instructions from the generative AI model based on the prompt, wherein the processing instructions comprise a sequence of steps for processing the SAR data (Fig. 5, processing flowchart),
by the natural language processing engine:
identifying processing operations, wherein a processing operation of the processing operations is identified for each step of the sequence of steps, and encoding a processing algorithm including the processing operations from the generative AI model (para. 47, “In some embodiments, the query preprocessing module can utilize large language models [LLMs] and/or natural language processing [NLPs] to generate a structured query from the user query. The structured query includes a variety of labels, depending on detected elements of the user query. For example, the structured query can include labels for subjects, locations, named entities, parts of speech, word classification and many more. In the first phase, the structured query with a first set of labels can be run through the knowledge graph 406, and/or queried against a variety of sources, such as the document storage 416, and if applicable/authorized against private indexes 418. When a root query is generated, the root query associated with the structured query can be searched against a vector embedding database/index 420. At the end of the first phase, a first set of results can be generated and stored in the results services 434.”; creation of a structured query encodes a processing algorithm with the data needed to perform searching operations, each operation involving one set of labels), and
by the data processing engine:
executing the processing algorithm to process the SAR data to determine a response to the request (para. 50, “The query execution module 412 sends and/or executes the query through the query pathways generated by the query planner module 410, where the structured query and/or the root query can be searched against indexes, or searched using database management systems, or searched using APIs.”).
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 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kallman in view of Cooper et al. (US 20260142671 A1), hereinafter Cooper.
Regarding claims 5 and 13, Kallman teaches the computing apparatus of claim 4 and the method of claim 12 respectively, but fails to teach
wherein to process the SAR data, the program instructions direct the computing apparatus to receive the processing operations from a processor library.
However, Cooper teaches
wherein to process the SAR data, the program instructions direct the computing apparatus to receive the processing operations from a processor library (para. 122, “Processor 1105 would store incoming data for processing on on-chip memory 1108, process the data using standardized sourceblock library 1103 and deconstruction/reconstruction algorithms 1104, and send the processed data to other hardware on device 1100.”; para. 157, “FIGS. 27A and 27B illustrate an exemplary architecture for an AI deblocking network configured to provide deblocking for a general N-channel data stream, according to an embodiment. The term ‘N-channel’ refers to data that is composed of multiple distinct channels of modalities, where each channel represents a different aspect of type of information. These channels can exist in various forms, such as sensor readings, image color channels, or data streams, and they are often used together to provide a more comprehensive understanding of the underlying phenomenon. Examples of N-channel data include, but is not limited to, […] SAR image data […] and more.”).
Kallman and Cooper are considered to be analogous to the claimed invention because they are in the same field of AI-processing of SAR data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kallman with the teachings of Cooper with the motivation of being able to effectively process large and/or diverse volumes of data.
Allowable Subject Matter
Claims 7-8, 15-16, and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC K HODAC whose telephone number is (571) 270-0123. The examiner can normally be reached M-Th 8-6.
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/ERIC K HODAC/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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