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 .
Status of Application
This office action is in response to the most recent filings filed by applicants on 05/21/26. This office action is a Final.
Claims 1, 10 and 19 are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-9 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 10-18 is/are directed to a computer program product which is a statutory category.
In the originally submitted specification describes the above limitation at least in [0045]: A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored. In light of the description above, the claim limitation above is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 19-20 is/are directed to a system which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 10 and 19, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, Independent Method Claim 1 is directed to an abstract idea, as evidenced by claim limitations “generating, a generative contextual model comprising a plurality of nodes; modeling, a solution chain based on the plurality of nodes of the generative contextual model; generating, a plurality of cost calculation matrixes for each node in the plurality of nodes; receiving, a user determined impact factor of the solution chain; determining, a dynamic weight for each cost calculation matrix in the plurality of cost calculation matrixes; determining, a final cost based on the impact factor and a sum of the dynamic weights for each cost calculation matrix; and executing the solution chain.”
In the originally submitted specification, [0001] Aspects of the present invention relate generally to asset maintenance tracking systems. [0002] Asset maintenance tracking may provide previous work order history and maintenance solutions.
In light of the specification, the claim limitations above belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing vehicle maintenance for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 10 and 19 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A computer-implemented method, comprising: by a processor set, by the processor set, A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
The above claim limitations are shown in the specification in [0062]-[0065]. For instance, [0062] The machine learning model 400 may be, for example, a large language model such as ChatGPT, that receives text values within domain knowledge and work history as inputs and outputs text relating to known repairs or part replacements, known non-effective repairs or part replacements, costs and timing associated with repairs and replacements, etc. The machine learning model 400 generates text, for example, based on a generative trained transformer algorithm. The machine learning model 400 may be configured to provide, as input to the generative contextual model 210, the output text relating to known repairs or part replacements, etc. The machine learning model 400 may be configured to generate the generative contextual model 210, which may be a computer-based program configured to capture or predict outcomes of solution chains based on domain knowledge (published manuals, technical descriptions, etc.) and working history (past service history of the asset, event, material, etc. associated with the node). Domain knowledge and work history may be stored, for example, in database 230 corresponding to remote database 130 depicted in FIG. 1. In embodiments, the generative contextual model 210 may be generated by associating governing equations with each of the domain knowledge and work history and extrapolating solution chains to identify the best solution to a repair or part replacement event. Governing equations may be any rules describing the interaction between variables in the model. The machine learning model 400 may identify solution chains within the generative contextual model comprising nodes and edges based on domain knowledge and working history. In embodiments, the machine learning model 400 may be trained on a plurality of solution chains, a plurality of pre-generated generative contextual models, and a plurality of cost calculation matrixes to improve the accuracy of generating the generative contextual model. For example, training the machine learning model 400 on executing solution chains and their corresponding generative contextual models and cost calculation matrixes may improve the likelihood a correct solution train is identified based on historical data of executing a particular solution chain.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claim 1 does not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
The additional elements of a “machine learning model”, “a generative contextual model”, “plurality of nodes of the generative contextual model”, “calculation matrix”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2-9, 11-18 and 20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein the generating the generative contextual model comprising the plurality of nodes is based on received sensor event data of a vehicle” and dependent claims 3 recite “wherein the dynamic weight is determined by dividing a total vector count by a matched vector count”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 5 recites “wherein the generating the generative contextual model comprises: associating governing equations, via a machine learning model, between a domain knowledge and a work history of the plurality of nodes; and extrapolating solution chains based on the associated governing equations to generate the generative contextual model”. Dependent claims 6 recites “further comprising training the machine learning model based on the solution chain, the plurality of generative contextual models, and the plurality of cost calculation matrixes to improve an accuracy of the generating the generative contextual model”. In this claim, “associating governing equations, via a machine learning model”, “cost calculation matrixes” are additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-9, 11-18 and 20 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A computer-implemented method, comprising: by a processor set, by the processor set, A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0062]-[0065]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 10 and 19 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-9, 11-18 and 20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 10 and 19. As a result, Examiner asserts that dependent claims, such as dependent claims 2-9, 11-18 and 20 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over ( US 2024/0095460 A1) Xu et al., and further in view of (US 2023/0259821 A1) Travalini et al.
As per claims 1, 10 and 19: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
A computer-implemented method (Reference Xu shows [0190] The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory 1004 may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device 1000. As used herein, computer storage media does not comprise signals per se.), comprising:
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to (Reference Xu shows [0190] The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory 1004 may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device 1000. As used herein, computer storage media does not comprise signals per se.):
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to (Reference Xu shows [0190] The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory 1004 may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device 1000. As used herein, computer storage media does not comprise signals per se.):
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
generating, by a processor set, a generative contextual model comprising a plurality of nodes (Reference Xu shows: [0021] For instance, a system(s) may receive audio data generated using one or more microphones of a vehicle, where the audio data represents speech (e.g., an utterance) from a user of the vehicle. In some examples, the speech may be associated with a task being requested by the user, such as a request to provide information associated with the vehicle. The system(s) may then process the audio data using a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.) that is configured to generate text data associated with the audio data. For instance, the text data may represent a transcript (e.g., one or more letters, words, symbols, numbers, etc.) associated with the speech and/or an indication as to which passenger(s)/user(s) is associated with the speech. For example, if the speech is associated with a request for information about the vehicle, such as “What tire pressure should I use for the front tires,” then the text data may represent the transcript of the speech. In some examples, the speech-processing model(s) may further generate the text data to represent additional information associated with the speech, such as an intent of the speech (e.g., get tire information) and/or information for a slot(s) (e.g., tire pressure) associated with the intent. [0029] The system(s) may then use the text data representing the transcript, data representing the question/answer pair(s), data representing the portion(s) of the contextual information, and/or additional data to generate a prompt associated with the speech. The system(s) may then input, into a language model(s) (e.g., a large language model(s)), prompt data representing the prompt. As described herein, the language model(s) may include any type of language model(s), such as a large language model (LLM), generative language model(s) (e.g., a Generative Pretrained Transformer (GPT), etc.), a representation language model(s) (e.g., a Bidirectional Encoder Representations from Transformers (BERT), etc.), and/or any other type of language model. The language model(s) may then process the prompt data and, based on the processing, output data associated with the speech. For example, if the speech represents a question associated with the vehicle, then the output data may represent information (e.g., an answer) associated with the question. The system(s) may then provide the output to the user, such as by outputting audio associated with the output using one or more speakers.);
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
modeling, by the processor set, a solution chain based on the plurality of nodes of the generative contextual model
In the originally submitted specification, the above limitation is described in [0019]: build generative contextual models to model real-time comprehensive solution chains based on work order history and domain knowledge. The system may build cost calculation matrixes for each node in the generative contextual model to evaluate maintenance options, adaptively discover solution chain and trace-back based on real-time dynamic context sensor or resource data, evaluate maintenance options based on a flexible cost impact factor and dynamic real-time sensor or data by leveraging the generative contextual model and cost calculation matrix, and provide transparent history trace to identify a solution chain.
In light of the above, Reference Xu shows a generative contextual model ([0021]) and [0028]: recommending maintenance - the retrieval system(s) may use one or more additional and/or alternative techniques. For a first example, the portions of the information may be separated into different categories. For instance, if the information (from a knowledge base) is associated with an OEM manual of a vehicle, then the portions of the information may be separated into component categories (e.g., tires, motors, doors, windows, etc.), feature categories (e.g., radios, displays, etc.), maintenance categories (e.g., time periods for recommended maintenance, etc.), and/or any other category. The retrieval system(s) may then use the categories to retrieve a portion(s) of the information that is in a similar category as the transcript represented by the text data. For a second example, the retrieval system(s) may match one or more words from the transcript represented by the text data to one or more words represented by one or more portions of the information. The retrieval system(s) may then retrieve a portion(s) of the information that includes at least a threshold number (e.g., one, two, three, five, ten, etc.) of matching words. [0034] In some examples, based on the processing, the speech-processing component(s) 102 may generate text data 106 representing one or more words (e.g., a transcript) associated with the speech. For example, if the audio data 104 represents a question that includes “What is the recommended tire pressure?” then the text data 106 may represent text that includes “what is the recommended tire pressure?” In some examples, the speech-processing component(s) 102 may further process the audio data 104 and/or the text data 106 to determine additional information associated with the speech. For instance, the speech-processing component(s) 102 may further determine an intent of the speech and/or information associated with one or more slots or tokens related to the intent. As described herein, an intent may include, but is not limited to, requesting information (e.g., information about a component, a feature, a maintenance schedule, etc.), scheduling an event (e.g., scheduling a maintenance appointment, etc.), and/or the like associated with the vehicle. In some embodiments, the text data may be generated from one or more inputs to a user interface, an input device (e.g., physical or digital keyboard), and/or the like, in addition to or alternatively from being generated based on audio data. [0035] For instance, FIG. 2 illustrates an example of the speech-processing component(s) 102 processing audio data 202 (which may represent, and/or include, the audio data 104) in order to generate text data 204 (which may represent, and/or include, the text data 106), in accordance with some embodiments of the present disclosure. As shown, the audio data 104 may represent at least speech from a user, where the speech is a question that includes “What is the recommended tire pressure?” As such, the speech-processing component(s) 102 may process the audio data 202 in order to generate the text data 204. As shown, the text data 204 may represent at least a transcript 206 or diarization of the speech, such as “what is the recommended tire pressure?” In some examples, the text data 204 may further represent an intent 208 associated with the speech, such as “requesting information.” In some examples, the text data 204 may further represent information for slots 210(1)-(3) associated with the intent 208, where the information includes “recommended” for the first slot 210(1), “tire” for the second slot 210(2), and “pressure for the third slot 210(3);
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
generating, by the processor set, a plurality of cost calculation matrixes for each node in the plurality of nodes
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation”.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”).
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
receiving, by the processor set, a user determined impact factor of the solution chain
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”. Here, weighting and probabilities reads on “impact factor” in the claim above.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses.
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
determining, by the processor set, a dynamic weight for each cost calculation matrix in the plurality of cost calculation matrixes
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses.
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
determining, by the processor set, a final cost based on the impact factor and a sum of the dynamic weights for each cost calculation matrix
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”. Here, weighting and probabilities reads on “impact factor” in the claim above.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses.
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A); and
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
executing, by the processor set, the solution chain (Reference Xu shows: [0111] The DLA(s) may quickly and efficiently execute neural networks, especially CNNs, on processed or unprocessed data for any of a variety of functions, including, for example and without limitation: a CNN for object identification and detection using data from camera sensors; a CNN for distance estimation using data from camera sensors; a CNN for emergency vehicle detection and identification and detection using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and/or a CNN for security and/or safety related events. [0114] The RISC cores may interact with image sensors (e.g., the image sensors of any of the cameras described herein), image signal processor(s), and/or the like. Each of the RISC cores may include any amount of memory. The RISC cores may use any of a number of protocols, depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and/or memory devices. For example, the RISC cores may include an instruction cache and/or a tightly coupled RAM. [0116] The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMA engines), and/or other peripherals. The vector processing subsystem may operate as the primary processing engine of the PVA, and may include a vector processing unit (VPU), an instruction cache, and/or vector memory (e.g., VMEM). A VPU core may include a digital signal processor such as, for example, a single instruction, multiple data (SIMD), very long instruction word (VLIW) digital signal processor. The combination of the SIMD and VLIW may enhance throughput and speed).
As per claims 2, 11 and 20: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
wherein the generating the generative contextual model comprising the plurality of nodes is based on received sensor event data of a vehicle (Reference Xu shows: [0111] The DLA(s) may quickly and efficiently execute neural networks, especially CNNs, on processed or unprocessed data for any of a variety of functions, including, for example and without limitation: a CNN for object identification and detection using data from camera sensors; a CNN for distance estimation using data from camera sensors; a CNN for emergency vehicle detection and identification and detection using data from microphones; a CNN for facial recognition and vehicle owner identification using data from camera sensors; and/or a CNN for security and/or safety related events. [0114] The RISC cores may interact with image sensors (e.g., the image sensors of any of the cameras described herein), image signal processor(s), and/or the like. Each of the RISC cores may include any amount of memory. The RISC cores may use any of a number of protocols, depending on the embodiment. In some examples, the RISC cores may execute a real-time operating system (RTOS). The RISC cores may be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and/or memory devices. For example, the RISC cores may include an instruction cache and/or a tightly coupled RAM. [0116] The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. The PVA core may include a processor subsystem, DMA engine(s) (e.g., two DMA engines), and/or other peripherals. The vector processing subsystem may operate as the primary processing engine of the PVA, and may include a vector processing unit (VPU), an instruction cache, and/or vector memory (e.g., VMEM). A VPU core may include a digital signal processor such as, for example, a single instruction, multiple data (SIMD), very long instruction word (VLIW) digital signal processor. The combination of the SIMD and VLIW may enhance throughput and speed).
As per claims 3 and 12: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
wherein the dynamic weight is determined by dividing a total vector count by a matched vector count (Reference Xu: [0041] In some examples, the retrieval component 108 may generate the embeddings 402 associated with the question/answer pairs. For instance, the retrieval component 108 may include an encoder(s) that transforms the question/answer pairs to dense vectors, where the embeddings 402 are associated with the dense vectors. In some examples, one or more other components and/or systems may generate the embeddings 402 associated with the question/answer pairs. For instance, the one or more other components and/or systems may include the encoder(s) that transforms the question/answer pairs to the dense vectors. In some examples, one or more of the embeddings 402 are generated based on receiving the text data 204 representing the transcript 206. In some examples, one or more of the embeddings 402 are generated before receiving the text data 204 representing the transcript 206. [0042] As further shown by the example of FIG. 4, the retrieval component 108 may further receive and/or generate an embedding 404 associated with the text data 204. For instance, and as described herein, the retrieval component 108 may include an encoder(s) that transforms the text data 204 to a dense vector, where the embedding 404 is associated with the dense vector. The retrieval component 108 may then use the embedding 404 associated with the text data 204 and the embeddings 402 associated with the question/answer pairs (e.g., all the question/answer pairs form the example of FIG. 3, although only six are illustrated for clarity reasons) to retrieve a threshold number of the question/answer pairs. For instance, the retrieval component 108 may use the embedding 404 and the embeddings 402 to identify the question/answer pairs that are the most similar to the transcript 206 represented by the text data 204. Also see, [0053], [0113], [0116]-[0117]).
As per claims 4 and 13: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
further comprising building a plurality of generative contextual models for each node in the plurality of nodes (Reference Xu: [0096]: A CAN bus may be configured to have dozens or even hundreds of nodes, each with its own unique identifier (e.g., a CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine revolutions per minute (RPMs), button positions, and/or other vehicle status indicators. The CAN bus may be ASIL B compliant. [0202] As shown in FIG. 11, the data center infrastructure layer 1110 may include a resource orchestrator 1112, grouped computing resources 1114, and node computing resources (“node C.R.s”) 1116(1)-1116(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s 1116(1)-1116(N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (NW I/O) devices, network switches, virtual machines (VMs), power modules, and/or cooling modules, etc. In some embodiments, one or more node C.R.s from among node C.R.s 1116(1)-1116(N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node C.R.s 1116(1)-11161(N) may include one or more virtual components, such as vGPUs, vCPUs, and/or the like, and/or one or more of the node C.R.s 1116(1)-1116(N) may correspond to a virtual machine (VM). Also see, [0203]-[0207]).
As per claims 5 and 14: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
wherein the generating the generative contextual model comprises:
associating governing equations, via a machine learning model, between a domain knowledge and a work history of the plurality of nodes (Reference Xu: [0113] The accelerator(s) 914 (e.g., the hardware acceleration cluster) may include a programmable vision accelerator(s) (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA(s) may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and/or any number of vector processors. [0116] The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. [0117]: In other examples, the vector processors that are included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, the plurality of vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may simultaneously execute different computer vision algorithms, on the same image, or even execute different algorithms on sequential images or portions of an image. Among other things, any number of PVAs may be included in the hardware acceleration cluster and any number of vector processors may be included in each of the PVAs. In addition, the PVA(s) may include additional error correcting code (ECC) memory, to enhance overall system safety. [0121]: In other words, the PVA performs well on semi-dense or dense regular computation, even on small data sets, which need predictable run-times with low latency and low power. Thus, in the context of platforms for autonomous vehicles, the PVAs are designed to run classic computer vision algorithms, as they are efficient at object detection and operating on integer math).
However, Reference Xu does not explicitly show “history” as is discussed in the claim above. Reference Travalini shows the above limitation at least in [0065] Referring to FIG. 5B, in an embodiment involving supervised machine learning, a data store (e.g., graph database) may store the relationships between collected training data and current data and other inputs. A supervised machine learning model may provide an indication to a graph module interfacing with the graph database that output from the ML model was correct and/or incorrect. In response to that indication, the graph module may update the graph database to improve the accuracy of predictions. The modifications may be based on historical data, a feedback loop, or from an external source, such as property management's preferences or rules sets, another computing device, or the like. Where feedback is received and causes the diagnosis engine to adjust its predictions, the machine learning model may be referred to as a supervised machine learning model. Although the aforementioned example refers to a graph database, the disclosure is not so limited. Other forms and types of data stores may be used with the diagnosis engine to assist in predicting and troubleshooting maintenance requests from the user's/resident input interface. For example, pathways 700n (e.g., programmatic rule sets) in the AI classification engine may be combined with data extracted from the resident user interface to suggest diagnosis name-value pairings for consideration in the diagnosis engine. [0069]-[0070], [0114], [0119]-[0120], [0132]-[0134].
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A); and
Regarding the claim limitations below, Reference Xu in view of Travalini shows:
extrapolating solution chains based on the associated governing equations to generate the generative contextual model (Reference Xu: [0113] The accelerator(s) 914 (e.g., the hardware acceleration cluster) may include a programmable vision accelerator(s) (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA(s) may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and/or any number of vector processors. [0116] The vector processors may be programmable processors that may be designed to efficiently and flexibly execute programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA may include a PVA core and two vector processing subsystem partitions. [0117]: In other examples, the vector processors that are included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, the plurality of vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may simultaneously execute different computer vision algorithms, on the same image, or even execute different algorithms on sequential images or portions of an image. Among other things, any number of PVAs may be included in the hardware acceleration cluster and any number of vector processors may be included in each of the PVAs. In addition, the PVA(s) may include additional error correcting code (ECC) memory, to enhance overall system safety. [0121]: In other words, the PVA performs well on semi-dense or dense regular computation, even on small data sets, which need predictable run-times with low latency and low power. Thus, in the context of platforms for autonomous vehicles, the PVAs are designed to run classic computer vision algorithms, as they are efficient at object detection and operating on integer math, also see: [00117], [0238]).
As per claims 6 and 15: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
further comprising training the machine learning model based on the solution chain, the plurality of generative contextual models, and the plurality of cost calculation matrixes to improve an accuracy of the generating the generative contextual model.
Reference Xu shows improving accuracy in [0063] In some examples, one or more techniques may be used to determine whether the dialogue system associated with the process 100 is accurate. For instance, an initial set of question/answer pairs may be generated, where a first portion of the initial set of question/answer pairs is associated with reference question/answer pairs and a second portion of the initial set of question/answer pairs is associated with testing question/answer pairs. Also see, [0064]-[0065], [0105], [0155]-[0156].
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” in the claim above.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”).
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A).
As per claims 7 and 16: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
further comprising adjusting each cost calculation matrix in the plurality of cost calculation matrixes based on the received impact factor.
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”. Here, weighting and probabilities reads on “impact factor” in the claim above.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses.
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A).
As per claims 8 and 17: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
further comprising determining a new final cost based on the received impact factor and a sum of each dynamic weight for each adjust cost calculation matrix.
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”. Here, weighting and probabilities reads on “impact factor” in the claim above.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses.
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A).
As per claims 9 and 18: Regarding the claim limitations below, Reference Xu in view of Travalini shows:
wherein each node in the plurality of nodes comprises an object name and an object type, and the generative contextual model comprises one or more edges linking each node in the plurality of nodes to another node based on linkage types.
Regarding the claim limitations above, Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”. Here, weighting and probabilities reads on “impact factor” in the claim above.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses. Travalini shows the “edges linking each node” [0058]: The first model may use named-entity recognition (NER) to identify the item and/or the symptom. A second model (e.g., a second, separate algorithm for NER) may be used to identify a location and/or a component. Both models may be trained using a series of keywords, synonyms, and/or phrases associated with the particular entity/node (e.g., the item, the symptom, the location, or the component). The identified entities/nodes may be fed into the one or more machine learning models to select a conversational pathway. Additionally or alternatively, the Problem Classification Engine may use an edge case model to identify problems, such as leaks, mold, mildew, and/or other uncommon cases. The edge case model may use a combination of encodings (tf-idf, word2vec) and algorithms (e.g., K-nearest neighbors (KNN), Naive Bayes (NB), etc.) to identify the edge cases and add the result to the first and/or second model. Based on the first and second models, a third model may be generated. The third model may be a specific classification model that expands classification to symptoms that are specific to an item (e.g., toilet-running, door-stuck, etc.). The third model may build on similar tools used to generate the edge case model. The third model may add more complicated algorithms, like Tensorflow-Keras neural networks and bidirectional encoder representation and transformers (BERT) encodings. In some instances, the classification set may be limited to item-specific options for symptom, component, and/or location (e.g., no hose component for door). This may be a rule-based symptom that dynamically pulls an item-specific model using a tool depending on a quantity of data. For example, Tensorflow may be used for large data sets (e.g., >1000), while naive-bayes/KNN may be used for smaller data sets. [0069], [0119];
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A).
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Applicant’s Argument #1
Applicants argue on page(s) 7-10 of applicants remarks that “Accordingly, based on the above excerpt, claim 1 applies any alleged abstract idea to an efficient handling of vehicle maintenance, and improves the functioning of computer-based maintenance systems. Therefore, claim 1 integrates the alleged abstract idea into a practical application, and is therefore directed to patent-eligible subject matter under Step 2A, Prong Two.” (see applicants remarks for more details).
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
When giving the claims broadest reasonable interpretation in light of the specification, applicants’ arguments above are unpersuasive. With respect to the Step 2A, Prong One, the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). Please see 101 rejection above for details under the Step 2A, prong one.
In light of the specification, the claim limitations above belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing vehicle maintenance for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 10 and 19 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above. The groupings are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Under step 2A, the groupings are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract. Further, applicants’ arguments related to 2A prong 2 and 2B have been addressed in the 101 rejection above.
The MPEP in 2106.04(a)(2) II Certain Methods of Organizing Human Activity, specifically as B. Commercial or Legal Interactions. "Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. Here, the MPEP recites two examples that are similar to the present application and have been shown by the courts to not pass the subject matter eligibility test under 35 U.S.C. 101.
An example of a claim reciting business relations is found in Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017). The business relation at issue in Credit Acceptance is the relationship between a customer and dealer when processing a credit application to purchase a vehicle. The patentee claimed a "system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user." 859 F.3d at 1054, 123 USPQ2d at 1108. The Federal Circuit described the claims as directed to the abstract idea of "processing an application for financing a loan" and found "no meaningful distinction between this type of financial industry practice" and the concept of intermediated settlement in Alice or the hedging concept in Bilski. 859 F.3d at 1054, 123 USPQ2d at 1108.
Another example of subject matter where the commercial or legal interaction is business relations includes, processing information through a clearing-house, where the business relation is the relationship between a party submitted a credit application (e.g., a car dealer) and funding sources (e.g., banks) when processing credit applications, Dealertrack v. Huber, 674 F.3d 1315, 1331, 101 USPQ2d 1325, 1339 (Fed. Cir. 2012).
The facts of the cases above are very similar to the claimed invention and as such, the claims are considered to be an abstract idea. Further, as is shown in the Step 2A prong 2 and Step 2B, the additional elements of the claim do not help the claimed invention overcome the 101 rejection above.
Applicant’s Argument #2
Applicants argue on page(s) 9-10 of applicants remarks that “Patent Eligibility Analysis under Step 2B Step 2B concerns analyzing whether the additional elements of the claims raise them as a whole to be directed towards "significantly more" than the identified abstract idea. However, as independent claim 1 has been found patent eligible under Step 2A Prong One and Two, the patent-eligibility analysis does not proceed to Step 2B.” (see applicants remarks for more details).
Response to Argument #2
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
While the amended claims are related to improvement to the business process, they are not an improvement to the field of machine learning or neural network methods.
Putting data through the machine learning model allows you to use better data, but this is not improving the field of the machine learning. The claims are directed to managing vehicle maintenance for one or more human entities and merely use a computer to improve the performance of that determination—not the performance of a computer. (See MPEP 2106.05(a)(II)(i); A commonplace business method or mathematical algorithm being applied on a general-purpose computer, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).
MPEP 2106.05f - Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363
MPEP 2106.05(f) iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
Applicant’s Argument #3
Applicants argue on page(s) 10-12 of applicants remarks that “However, Xu does not teach or suggest generating a generative contextual model comprising a plurality of nodes or modeling a solution chain based on the plurality of nodes. The outputs of Xu are transcripts, intents, and responses, which are unstructured or semi-structured textual data. Xu instead of merely transforming user speech/query into textual queries, also does the intent recognition by capturing the key words of the query and then retrieving information responsive to the keyword of that query but does not construct the plurality of nodes which leads to a solution chain. Hence, retrieval of categorized information or matching content does not constitute modeling a solution chain. In contrast, claim 1 requires generating a generative contextual model comprising a plurality of nodes to model a solution chain. Further, Xu describes executing neural networks, such as CNNs, for perception and processing tasks (see, cited portion on pg. 34-35 of the Office Action, and ([0111] of Xu). However, such execution relates to running computational models for tasks such as object detection or signal processing. Xu does not teach or suggest executing the solution chain. Execution of neural networks is not equivalent to executing a solution chain (repair steps) derived from a generative contextual model, as required by claim 1.” (see applicants remarks for more details).
Response to Argument #3
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
Applicants are arguing the following claim limitations:
“receiving, by the processor set, a user determined impact factor of the solution chain”
Firstly, it should be noted that applicants’ arguments that Reference Xu is not showing the above argued claim limitation are unpersuasive because applicants are pointing to OA dated 02/26/26 pages 34-35 and paragraph [0111] of Reference Xu, but these pages of the OA and the paragraph argued above are related to the rejection of a different claim limitation. The pages argued are related to the rejection of claim limitation “executing, by the processor set, the solution chain”.
Secondly, it should be noted that applicants originally submitted specification and broadly recited claims do not support the narrow interpretation of the claim argued by applicants above. For instance, the originally submitted specifications shows: [0016] Aspects of the present invention relate generally to asset maintenance tracking (AMT) systems and, more particularly, to a system, method, or computer program product for determining an optimal solution to vehicle maintenance or repairs. According to aspects of the present invention, an AMT system may receive sensor event data, such as a low-tire pressure warning, and identify a problematic asset based on the sensor event data. The AMT system may build a generative contextual model from the sensor event data. The generative contextual model may be a computer-based program configured to capture or predict outcomes based on the sensor event data and a cost calculation matrix. In embodiments, the generative contextual model is generated by associating governing equations with the sensor event data and a cost calculation matrix in order to identify solutions to the sensor event data. In embodiments, the cost calculation matrix may be generated for each node (an asset, event, material, etc.,) within the generative contextual model by compiling asset cost, repair/replacement cost, etc. The generative contextual model and cost calculation matrix may be used to identify solution chains for resolving maintenance or repairs that also consider a user budget and a user input. In embodiments, solution chains may be a single event or sequence of events, including initial events, intermediate events, and final results, of vehicle maintenance repairs or part replacements. In further embodiments, the vehicle maintenance repairs or part replacements may include costs and timing. [0020]: In some embodiments, solution chains consist of multiple nodes and edges as a series of repair or replacement steps identified as a time and cost-effective solution.
In light of the specification, Reference Xu in view of Reference Travalini shows. Reference Xu shows solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities. However, Reference Xu does not explicitly show “cost calculation” and “dynamic”. Here, weighting and probabilities reads on “impact factor” in the claim above.
Here, the Reference Xu shows solution recommendations which reads on the above claim limitation. For instance, in [0021]: Xu shows capturing intent of the speech, [0028], [0034]-[0035]: shows making recommendations. Also, see [0103], [0124], [0133], and [0209].
In [0034] Reference Xu recites: [0034] In some examples, based on the processing, the speech-processing component(s) 102 may generate text data 106 representing one or more words (e.g., a transcript) associated with the speech. For example, if the audio data 104 represents a question that includes “What is the recommended tire pressure?” then the text data 106 may represent text that includes “what is the recommended tire pressure?” In some examples, the speech-processing component(s) 102 may further process the audio data 104 and/or the text data 106 to determine additional information associated with the speech. For instance, the speech-processing component(s) 102 may further determine an intent of the speech and/or information associated with one or more slots or tokens related to the intent. As described herein, an intent may include, but is not limited to, requesting information (e.g., information about a component, a feature, a maintenance schedule, etc.), scheduling an event (e.g., scheduling a maintenance appointment, etc.), and/or the like associated with the vehicle. In some embodiments, the text data may be generated from one or more inputs to a user interface, an input device (e.g., physical or digital keyboard), and/or the like, in addition to or alternatively from being generated based on audio data. Here, scheduling a maintenance appt based on speech processing reads on the claim above in light of the specification.
Reference Travalini shows “cost calculation” in [0027] A framework for machine learning algorithm may involve a combination of one or more components, sometimes three components: (1) representation, (2) evaluation, and (3) optimization components. Representation components refer to computing units that perform steps to represent knowledge in different ways, including but not limited to as one or more decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and/or others. Evaluation components refer to computing units that perform steps to represent the way hypotheses (e.g., candidate programs) are evaluated, including but not limited to as accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence, and/or others. Optimization components refer to computing units that perform steps that generate candidate programs in different ways, including but not limited to combinatorial optimization, convex optimization, constrained optimization, and/or others. In some embodiments, other components and/or sub-components of the aforementioned components may be present in the system to further enhance and supplement the aforementioned machine learning functionality. [0064]: The pricing module may determine an estimated cost of a job based on local pricing for supplies, a billing rate based on the skill set required for the job, and/or the number of hours required for the job. [0094]: an amount of time needed to fix the problem, an estimated cost associated with fixing the problem, user preferences associated with fixing the problem, an estimate cost associated with the technician and/or vendor, an estimated of the tools, parts, and/or corresponding SKUs needed to fix the problem, an identification of the symptom (e.g., “cloggedID”), and/or a recommended solution (e.g., “Snake the toilet”). Travalini shows “dynamic” at least in [0085] Each of the nodes may be connected to one or more other nodes. The connections may connect the output of a node to the input of another node. A connection may be correlated with a weighting value. For example, one connection may be weighted as more important or significant than another, thereby influencing the degree of further processing as input traverses across the artificial neural network. Such connections may be modified such that the artificial neural network 100 may learn and/or be dynamically reconfigured. Though nodes are depicted as having connections only to successive nodes in FIG. 8, connections may be formed between any nodes. For example, one processing node may be configured to send output to a previous processing node. [0089] The artificial neural network 100 may be dynamically modified to learn and provide better input. Based on, for example, previous input and output and feedback from the feedback system 150, the artificial neural network 100 may modify itself. For example, processing in nodes may change and/or connections may be weighted differently. Following on the example provided previously, the facial prediction may have been incorrect because the photos provided to the algorithm were tinted in a manner which made all faces look red. As such, the node which excluded sections of photos containing large contiguous sections of the color red could be considered unreliable, and the connections to that node may be weighted significantly less. Additionally or alternatively, the node may be reconfigured to process photos differently. The modifications may be predictions and/or guesses by the artificial neural network 100, such that the artificial neural network 100 may vary its nodes and connections to test hypotheses.
Reference Xu and Reference Travalini are analogous prior art to the claimed invention because the references generally relate to field of managing maintenance on property. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Travalini, particularly the ability to perform cost calculations: [0027], [0064], [0094], in the disclosure of Reference Xu, particularly in the ability to make solution recommendations as is discussed above in [0021], [0028], [0034], [0035]. Reference Xu further performs in [0041]-[0042]: analysis for different constraints. [0103]: deep learning matrix arithmetic, [0124], [0133] and [0209]: which shows weighting and probabilities, in order to provide for a system that not only recommends solutions but also costs associated with solutions as taught by Reference Travalini (see at least in [0027], [0064], [0094]), where upon the execution of the method and system of Reference Travalini for advanced diagnostics, generate work orders with a higher degree of accuracy, advise tenants and/or homeowners on do-it-yourself (DIY) solutions, and/or build intelligent home profiles using the conversational prompts so that the process of managing maintenance on property can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar managing maintenance on property field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Xu in view of Reference Travalini, the results of the combination were predictable (MPEP 2143 A)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
Ge M, Ohtani K, Ding M, Niu Y, Zhang Y, Takeda K. Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints. Sensors. 2024; 24(22):7323. https://doi.org/10.3390/s24227323
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics.
Foreign Reference:
KR 20210058713 A. VEHICLE PREDICTIVE MANAGEMENT SYSTEM USING VEHICLE DATA AND MOBILE PLATFORM. Lee Jeong Woo. The present invention relates to maintenance of a vehicle wherein by collecting and classifying data using sensors installed in the vehicle and a sensor such as a user's smartphone and analyzing thereof using an artificial intelligence technology, the present invention has an effect of being able to perform necessary maintenance in advance by identifying and predicting a state of the vehicle. The vehicle predictive maintenance system comprises: a CAN interface device; an OBD interface device; a user terminal; and a maintenance server.
Applicant's amendment necessitated the new ground(s) 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/N.N.P/Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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