DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 6, 2025 has been entered. Claims 1 – 21 are pending and have been examined.
Response to Amendments
In the reply filed 6/6/25, claims 1, 11 and 21 were amended. Accordingly, claims 1 – 21 are pending.
Response to Arguments
Applicant's arguments with respect to claims 1 – 21 have been carefully considered but are moot and not deemed persuasive in view of rejections below.
Examiner maintains the 101 and 103 rejections and has updated the detailed rejections below. However, examiner respectfully disagrees with applicant’s arguments on pages 7 – 9, that prior art fails to teach, vehicle signals from sensors or controls of a vehicle and providing them to an external entity. Iwasaki specifically teaches, generating a signal of a vehicle (Iwasaki [0805]: The order reception processor d720 transmits a control signal of the compartment based on a control request or information regarding vehicle exterior display or vehicle interior display of the contents for each compartment to the vehicle management device d270 and causes the vehicle d200 to perform predetermined contents display.) by one of a sensor and a control of a vehicle (Iwasaki [0242]: The gateway device 121b can acquire various pieces of information from the communication device 201, the HMI 202, a seat device 203, a collision sensor 204, an airbag device 205, and the controller of the upper structure 200 via the gateway device 209b of the upper structure 200. The gateway device 121b is another example of an “acquirer”. For example, the gateway device 121b acquires information indicating an operation input to the driving operator 202a of the HMI 202 from the HMI 202 via the gateway device 209b of the upper structure 200. The gateway device 121b outputs the information acquired from the HMI 202 to the automated driving controller 115. In this way, the automated driving controller 115 can control the travel of the vehicle 100 on the basis of the information indicating the operation input to the driving operator 202a of the HMI 202.), wherein the signal of the vehicle indicates one of an operational state (Iwasaki [0295]: The use state detector 202e detects a use state of each compartment of the vehicle 100 on the basis of the presence or the position of a user boarding on the vehicle 100 or a load. A compartment is a space in the upper structure 200 of the vehicle 100 and is a space which a user can enter or in which a load can be loaded. The use state detector 202e may detect the use state of the compartment on the basis of the detection results obtained by a load sensor and an in-vehicle camera (which are not illustrated).), an equipment state, and an environmental state of the vehicle sensed or produced by the one of the sensor and the control of the vehicle (Iwasaki [0021]: “In the above-described aspect (1), the communicator receives a service signal transmitted from the service provider, and the vehicle further includes: a compartment control signal generator configured to generate a control signal corresponding to the received service signal and transmit the control signal to equipment arranged in the compartment; and a driving control signal generator configured to transmit a control signal corresponding to the service signal to the controller.” Here, the signal of the vehicle is similar to the control signals transmitted and received to and from regarding the vehicle service, equipment in the vehicle compartment and the driving state control signals.). Therefore, examiner is not persuaded.
All claims have been updated below with clarifying prior art citations. Kindly let me know if you have any questions. Thanks.
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 – 21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category - MPEP §§ 2106.03
Claims 1–10 recite a method (process), claims 11–20 recite a system (machine), and claim 21 recites a non-transitory computer readable medium (manufacture), which fall within the statutory categories under § 101.
Independent Claims 1, 11 and 21 Analysis
Step 2A, Prong 1: Recited Judicial Exception (abstract idea) - MPEP §§ 2106.04(II)(A)(1), 2106.04(a)(2)
The representative claim 1 recites the following abstract idea limitations:
generating a signal of the vehicle by one of a sensor and a control of a vehicle, wherein the signal of the vehicle indicates one of an operational state, an equipment state, and an environmental state of the vehicle sensed or produced by the one of the sensor and the control of the vehicle (as drafted, this limitation is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the limitation precludes the step from practically being performed in the mind. This limitation, in the context of this claim, encompasses the user thinking about generating a signal. Thus, this limitation recites an abstract mental process under 2019 PEG because it can be performed in the human mind either through observation, evaluation and judgment and opinion);
wherein the mapping is performed using a matching and scoring algorithm utilizing multiple attributes of the vehicle signal identifier and the specification signal identifier selected from signal name, signal descriptive text, signal units, and signal data type (as drafted, this limitation is a method that, under its broadest reasonable interpretation, encompasses mathematical concepts. For example, “scoring” in the context of this claim encompasses performing mathematical calculations to score the signals. Therefore, the limitation recites a mathematical concept. Thus, this limitation recites an abstract mathematical concept under 2019 PEG because it can be performed using scoring algorithm.);
Step 2A, Prong 2: Integration into a Practical Application (additional elements) - MPEP §§ 2106.04(II)(A)(2), MPEP 2106.04(d), 2106.05(a)-(c),(e)-(h).
The claim recites the following additional elements:
using translation instructions stored in a memory and executed by a processor of the vehicle or resident in a cloud network (These steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of data receiving and data processing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 (see MPEP 2106.05(f)).),
receiving a vehicle signal identifier associated with the signal of the vehicle generated by the one of the sensor and the control of the vehicle and mapping the vehicle signal identifier to a corresponding specification signal identifier associated with an external standard (This limitation amounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).);
transmitting the signal of the vehicle from the vehicle to an external entity, wherein the transmitted signal of the vehicle is identified to the external entity by the corresponding specification signal identifier after the mapping of the received vehicle signal identifier is performed (This limitation is merely data transmission using computer as a tool which is considered to be insignificant extra solution activity (MPEP 2106.05(g).)).
Step 2B: Significantly more or amounting to an inventive concept (Transformation or Technological Improvement) - MPEP §§ 2106.05
Claim recites additional elements of “…memory and executed by a processor of the vehicle…” at a very high level of generality and without imposing meaningful limitations on the scope of the claim. In addition, paragraphs 30 – 35 of the instant specification describes generic off-the-shelf computer-based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".)
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well-understood, routine, and conventional manner.
MPEP § 2106.05 (d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g. at a high level of generality) as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc... ; buySAFE, Inc. v. Google, Inc...;
Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life...;
Electronic recordkeeping, Alice Corp...; Ultramercial... ;
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc...;
Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank...; and
A web browser's back and forward button functionality, Internet Patent Corp. v. Active Network, Inc...
Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Dependent Claims Analysis
The dependent claims have been fully considered as well, however, similar to the findings for independent claims above, these claims are similarly directed to the above-mentioned groupings of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea. Specifically,
Claims 2 and 12 add the limitation of “wherein the vehicle signal is received based on a vehicle identification that defines a plurality of extracted candidate vehicle signals.” This limitation amounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
Claims 3 and 13 add the limitation, “wherein the vehicle signal is received based on a specified use case providing a schema associated with the vehicle signal that defines a plurality of extracted candidate vehicle signals.” This limitation amounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
Claims 4 and 14 add the limitation, “caching a result of the mapping in a database stored in the memory of the vehicle or resident in the cloud for future use.” These steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 (see MPEP 2106.05(f)).
Claims 5 and 15 add the limitation, “based on a result of the mapping, providing the vehicle signal to the external entity labeling the vehicle signal with the specification signal identifier.” These steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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 (see MPEP 2106.05(f)).
Claims 6 and 16 add the limitation, “wherein the matching and scoring algorithm is operable for assessing a Levenshtein distance modified to fit signal name mapping between a signal name of the vehicle signal identifier and a signal name of the specification signal identifier.” This limitation is a method that, under its broadest reasonable interpretation, encompasses mathematical concepts. For example, “scoring” in the context of this claim encompasses performing mathematical calculations to score the signals. Therefore, the limitation recites a mathematical concept. Thus, this limitation recites an abstract mathematical concept under 2019 PEG because it can be performed using scoring algorithm.
Claims 7 and 17 add the limitation, “wherein the matching and scoring algorithm is operable for assessing a similarity between signal descriptive text of the vehicle signal identifier and signal descriptive text of the specification signal identifier using a machine learning model trained with automotive language.” This limitation is a method that, under its broadest reasonable interpretation, encompasses mathematical concepts. For example, “scoring” in the context of this claim encompasses performing mathematical calculations to score the signals. Therefore, the limitation recites a mathematical concept. Thus, this limitation recites an abstract mathematical concept under 2019 PEG because it can be performed using scoring algorithm.
Claims 8 and 18 add the limitation, “wherein the matching and scoring algorithm is operable for assessing a similarity between signal units of the vehicle signal identifier and signal units of the specification signal identifier using a heuristic matching algorithm.” This limitation is a method that, under its broadest reasonable interpretation, encompasses mathematical concepts. For example, “scoring” in the context of this claim encompasses performing mathematical calculations to score the signals. Therefore, the limitation recites a mathematical concept. Thus, this limitation recites an abstract mathematical concept under 2019 PEG because it can be performed using scoring algorithm.
Claims 9 and 19 add the limitation, “wherein the matching and scoring algorithm is operable for assessing a similarity between a signal data type of the vehicle signal identifier and a signal data type of the specification signal identifier using a data structure matching algorithm.” This limitation is a method that, under its broadest reasonable interpretation, encompasses mathematical concepts. For example, “scoring” in the context of this claim encompasses performing mathematical calculations to score the signals. Therefore, the limitation recites a mathematical concept. Thus, this limitation recites an abstract mathematical concept under 2019 PEG because it can be performed using scoring algorithm.
Claims 10 and 20 add the limitation, “wherein the matching and scoring algorithm is operable for weighting mapping results associated with each of the multiple attributes of the vehicle signal identifier and the specification signal identifier.” This limitation is a method that, under its broadest reasonable interpretation, encompasses mathematical concepts. For example, “scoring” in the context of this claim encompasses performing mathematical calculations to score the signals. Therefore, the limitation recites a mathematical concept. Thus, this limitation recites an abstract mathematical concept under 2019 PEG because it can be performed using scoring algorithm.
Therefore, claims 1–21 are directed to an abstract idea and do not recite additional elements sufficient to amount to significantly more. The dependent claims provide specific implementations but do not transform the abstract idea into a patent-eligible application. Therefore, the claims are not patent-eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 21 are rejected under 35 U.S.C. 103 as being unpatentable over Iwasaki et al., U.S. Patent Application Publication No.: 2020/0159251 (Hereinafter “Iwasaki”), and further in view of Moore et al., U.S. Patent Application Publication No.: 2019/0005151 (Hereinafter “Moore”).
Regarding claim 1, Iwasaki teaches, a method for vehicle unique signal to vehicle general signal matching (Iwasaki [0030]: “… interface controller configured to control information delivery between the vehicle and the service provider by transmitting a control signal to the vehicle on the basis of the service information.”), the method comprising:
generating a signal of a vehicle (Iwasaki [0805]: The order reception processor d720 transmits a control signal of the compartment based on a control request or information regarding vehicle exterior display or vehicle interior display of the contents for each compartment to the vehicle management device d270 and causes the vehicle d200 to perform predetermined contents display.) by one of a sensor and a control of a vehicle (Iwasaki [0242]: The gateway device 121b can acquire various pieces of information from the communication device 201, the HMI 202, a seat device 203, a collision sensor 204, an airbag device 205, and the controller of the upper structure 200 via the gateway device 209b of the upper structure 200. The gateway device 121b is another example of an “acquirer”. For example, the gateway device 121b acquires information indicating an operation input to the driving operator 202a of the HMI 202 from the HMI 202 via the gateway device 209b of the upper structure 200. The gateway device 121b outputs the information acquired from the HMI 202 to the automated driving controller 115. In this way, the automated driving controller 115 can control the travel of the vehicle 100 on the basis of the information indicating the operation input to the driving operator 202a of the HMI 202.),
wherein the signal of the vehicle indicates one of an operational state (Iwasaki [0295]: The use state detector 202e detects a use state of each compartment of the vehicle 100 on the basis of the presence or the position of a user boarding on the vehicle 100 or a load. A compartment is a space in the upper structure 200 of the vehicle 100 and is a space which a user can enter or in which a load can be loaded. The use state detector 202e may detect the use state of the compartment on the basis of the detection results obtained by a load sensor and an in-vehicle camera (which are not illustrated).), an equipment state, and an environmental state of the vehicle sensed or produced by the one of the sensor and the control of the vehicle (Iwasaki [0021]: “In the above-described aspect (1), the communicator receives a service signal transmitted from the service provider, and the vehicle further includes: a compartment control signal generator configured to generate a control signal corresponding to the received service signal and transmit the control signal to equipment arranged in the compartment; and a driving control signal generator configured to transmit a control signal corresponding to the service signal to the controller.” Here, the signal of the vehicle is similar to the control signals transmitted and received to and from regarding the vehicle service, equipment in the vehicle compartment and the driving state control signals.);
using translation instructions stored in a memory and executed by a processor of the vehicle or resident in a cloud network (Iwasaki [0226]: “… processor such as a central processor (CPU) or the like executes a program (software) stored in a memory.”),
receiving a vehicle signal identifier associated with the signal of the vehicle (Iwasaki [0030]: “… an interface controller configured to control information delivery between the vehicle and the service provider by transmitting a control signal to the vehicle on the basis of the service information.”) generated by the one of the sensor and the control of the vehicle (Iwasaki [0026]: In the above-described aspect (1), the controller includes an automated driving controller configured to perform automated driving on the basis of an external environment detection sensor and a position information sensor, and the interface permits the service user to use information when the automated driving is being executed and the service user is in the vehicle.)and mapping the vehicle signal identifier to a corresponding specification signal identifier associated with an external standard (Iwasaki [0254]: The connecting mechanism controller 119 operates the connector 151 on the basis of the control of the automated driving controller 115, for example. That is, the connecting mechanism controller 119 switches the state of the vehicle 100 between a state in which the upper structure 200 is fixed to the traveling device 110 and a state in which the upper structure 200 is detached from the traveling device 110 by controlling the connector 151.);
Iwasaki doesn’t clearly teach, wherein the mapping is performed using a matching and scoring algorithm utilizing multiple attributes of the vehicle signal identifier and the specification signal identifier selected from signal name, signal descriptive text, signal units, and signal data type. However, Moore [0040–0041] teaches, “As will be appreciated, there can be many different names associated with different components (and tags representing them) for an asset. As a result, it is not possible to perform a simple keyword matching process in order to identify the same tag from different virtual assets. In FIG. 3 illustrates a tag mapping process 300 in accordance with an example embodiment. In a typical tag mapping process, a user manually views two virtual models and identifies tags that are equivalent between the two virtual models. The example embodiments automate this process using machine learning, and provide recommendations for the customer (e.g., top 10 suggestions, etc.). Based on the feedback the models can be retrained. Known mappings can be used as training data to generate the models. Examples of natural language processing algorithms for tag mapping include N-grams, language modeling, classification, vector space model, latent semantic analysis, similarity scoring methods (e.g., Jaccard, Overlap, Jaro-Winkler, etc.), and latent Dirichlet allocation. Different algorithms can work better for different assets/data.”). and
transmitting the signal of the vehicle from the vehicle to an external entity, wherein the transmitted signal of the vehicle is identified to the external entity by the corresponding specification signal identifier after the mapping of the received vehicle signal identifier is performed (Moore [0027]: For example, an asset can be outfitted with one or more sensors configured to monitor respective operations or conditions. Data from the sensors can be recorded or transmitted to a cloud-based or other remote computing environment.).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Iwasaki et al. to the Moore’s system by adding the feature of mapping. The references (Iwasaki and Moore) teach features that are analogous art and they are directed to the same field of endeavor, such as databases. Ordinary skilled artisan would have been motivated to do so to provide Iwasaki’s system with enhanced data. (See Moore [Abstract], [0023], [0040-0041], [0057]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 2, the method of claim 1, wherein the vehicle signal is received based on a vehicle identification that defines a plurality of extracted candidate vehicle signals (Iwasaki [0712]: FIG. 82 is a diagram showing an example of content of the vehicle allocation table d173. In the vehicle allocation table d173, a vehicle attribute ID and the number of vehicles for each vehicle are associated with the area ID and the event ID. The area ID is identification information for identifying an area. The vehicle attribute ID is identification information for identifying an attribute of the vehicle d200 participating in the event. The attribute of the vehicle is, for example, an attribute indicating a genre of a service and specific content of the service provided by the vehicle d200 or a cargo stand of the vehicle d200. The content of the attribute may be stored as a vehicle attribute table d174 in the storage d170.)
Regarding claim 3, the method of claim 1, wherein the vehicle signal is received based on a specified use case providing a schema associated with the vehicle signal that defines a plurality of extracted candidate vehicle signals (Iwasaki [1262]: Also, the display controller i281 causes the vehicle exterior display i282 to execute a display form specified by the service provider S with respect to various types of control signals. The display controller i281 may set a condition to be displayed on the vehicle exterior display i282 on the basis of the display condition data i285a.).
Regarding claim 4, the method of claim 1, further comprising caching a result of the mapping in a database stored in the memory of the vehicle or resident in the cloud for future use (Iwasaki [1056]: The output g171 includes, for example, one or more of a projector, a display, a speaker, and the like. The reproduction data differs according to the reproduction means in the vehicle g100. For example, data to be projected onto an interior surface of the vehicle 100 if the reproduction means in the vehicle g100 is a projector, data to be displayed on a display if the reproduction means in the vehicle g100 is a display, and the like are included. Also, the output g171 may reproduce the room environment using projection mapping technology.).
Regarding claim 5, the method of claim 1, further comprising, based on a result of the mapping, providing the vehicle signal to the external entity labeling the vehicle signal with the specification signal identifier (Iwasaki [0030]: “According to an aspect of the present invention, there is provided a service management device including an identifier configured to identify a vehicle used by a service provider and a vehicle information provider configured to transmit information about the vehicle corresponding to the vehicle identified by the identifier to the service provider, the service management device including: a service information input configured to input service information of the service provider; and an interface controller configured to control information delivery between the vehicle and the service provider by transmitting a control signal to the vehicle on the basis of the service information.”).
Regarding claim 6, the method of claim 1, wherein the matching and scoring algorithm is operable for assessing a Levenshtein distance modified to fit signal name mapping between a signal name of the vehicle signal identifier and a signal name of the specification signal identifier (Moore [0058]: “In 740, the performing of the tag mapping may include generating a ranking for each candidate tag included in the reduced amount of tokenized tag records. The ranking may be a probability that a candidate tag is a match for the target tag. The ranking may be performed on various different algorithms (or combination of algorithms) which can be adjusted by a user. Also, different algorithms may have better accuracy for different assets, customers, use cases, and the like.”).
Regarding claim 7, the method of claim 1, wherein the matching and scoring algorithm is operable for assessing a similarity between signal descriptive text of the vehicle signal identifier and signal descriptive text of the specification signal identifier using a machine learning model trained with automotive language (Moore [0041]: “In FIG. 3 illustrates a tag mapping process 300 in accordance with an example embodiment. In a typical tag mapping process, a user manually views two virtual models and identifies tags that are equivalent between the two virtual models. The example embodiments automate this process using machine learning and provide recommendations for the customer (e.g., top 10 suggestions, etc.). Based on the feedback the models can be retrained. Known mappings can be used as training data to generate the models. Examples of natural language processing algorithms for tag mapping include N-grams, language modeling, classification, vector space model, latent semantic analysis, similarity scoring methods (e.g., Jaccard, Overlap, Jaro-Winkler, etc.), and latent Dirichlet allocation. Different algorithms can work better for different assets/data.”).
Regarding claim 8, the method of claim 1, wherein the matching and scoring algorithm is operable for assessing a similarity between signal units of the vehicle signal identifier and signal units of the specification signal identifier using a heuristic matching algorithm (Moore [0023]: “The tag may include alphanumeric characters or a grouping of words which are used to identify the component. The tag mapping process may incorporate information retrieval techniques and algorithms to narrow down a search space of potential tag matches for a target tag. Furthermore, a high fidelity algorithm may be performed to accurately determine a ranking for the remaining candidate tags, and a predetermined amount of the highest ranking candidate tags (e.g., top 10 candidate tags) may be output as possible matches for the target tag. The algorithm provides an automated process for matching together tags representing a same component from different virtual assets having different naming conventions. By grouping together tags, the tags can be analyzed together which can provide more analysis and understanding of assets.”).
Regarding claim 9, the method of claim 1, wherein the matching and scoring algorithm is operable for assessing a similarity between a signal data type of the vehicle signal identifier and a signal data type of the specification signal identifier using a data structure matching algorithm (Moore [0067]: “The processor 920 may perform a tag mapping process for one target tag in comparison to a plurality of candidate tags. As another example, the processor 920 may perform a tag mapping process for a plurality of target tags based on a plurality of candidate tags. In this later example, the processor 920 may receive a first document (e.g., target document) including a plurality of target tag records and a second document (e.g., master document) including a plurality of candidate tags.”).
Regarding claim 10, the method of claim 1, wherein the matching and scoring algorithm is operable for weighting mapping results associated with each of the multiple attributes of the vehicle signal identifier and the specification signal identifier (Moore [0062]: “In 840, the method includes executing the algorithm ensemble on input data in an order defined by the linking to generate a processing result of the input data. For example, the executing of the algorithm ensemble may be performed by a frame manager that is unique to the respective algorithm ensemble, and the frame manager may be configured to manage data as it moves between the algorithms included in the algorithm ensemble. As one non-limiting example, the executed algorithm ensemble may perform a tag mapping data processing operation, and each algorithm in the algorithm ensemble may be associated with at least one phase from among a plurality of phases included in the tag mapping data processing operation. One of the benefits of the method of FIG. 8 is that a customer can replace one or more algorithms while leaving the remaining algorithms/data of the process the same. This provides the customer a lot of flexibility.”).
Regarding claim 11, Iwasaki teaches, a system for vehicle unique signal to vehicle general signal matching (Iwasaki [0030]: “… interface controller configured to control information delivery between the vehicle and the service provider by transmitting a control signal to the vehicle on the basis of the service information.”), the system comprising:
one of a sensor and a control of a vehicle generating a signal of the vehicle (Iwasaki [0805]: The order reception processor d720 transmits a control signal of the compartment based on a control request or information regarding vehicle exterior display or vehicle interior display of the contents for each compartment to the vehicle management device d270 and causes the vehicle d200 to perform predetermined contents display.),
wherein the signal of the vehicle indicates one of an operational state (Iwasaki [0295]: The use state detector 202e detects a use state of each compartment of the vehicle 100 on the basis of the presence or the position of a user boarding on the vehicle 100 or a load. A compartment is a space in the upper structure 200 of the vehicle 100 and is a space which a user can enter or in which a load can be loaded. The use state detector 202e may detect the use state of the compartment on the basis of the detection results obtained by a load sensor and an in-vehicle camera (which are not illustrated).), an equipment state, and an environmental state of the vehicle sensed or produced by the one of the sensor and the control of the vehicle (Iwasaki [0021]: “In the above-described aspect (1), the communicator receives a service signal transmitted from the service provider, and the vehicle further includes: a compartment control signal generator configured to generate a control signal corresponding to the received service signal and transmit the control signal to equipment arranged in the compartment; and a driving control signal generator configured to transmit a control signal corresponding to the service signal to the controller.” Here, the signal of the vehicle is similar to the control signals transmitted and received to and from regarding the vehicle service, equipment in the vehicle compartment and the driving state control signals.);
a memory of the vehicle or resident in a cloud network; a processor of the vehicle or resident in the cloud network; and translation instructions stored in the memory and executed by the processor of the vehicle or resident in the cloud network operable for (Iwasaki [0226]: “… processor such as a central processor (CPU) or the like executes a program (software) stored in a memory.”)
receiving a vehicle signal identifier associated with the signal of the vehicle (Iwasaki [0030]: “… an interface controller configured to control information delivery between the vehicle and the service provider by transmitting a control signal to the vehicle on the basis of the service information.”) generated by the one of the sensor and the control of the vehicle (Iwasaki [0026]: In the above-described aspect (1), the controller includes an automated driving controller configured to perform automated driving on the basis of an external environment detection sensor and a position information sensor, and the interface permits the service user to use information when the automated driving is being executed and the service user is in the vehicle.) and mapping the vehicle signal identifier to a corresponding specification signal identifier associated with an external standard (Iwasaki [0254]: The connecting mechanism controller 119 operates the connector 151 on the basis of the control of the automated driving controller 115, for example. That is, the connecting mechanism controller 119 switches the state of the vehicle 100 between a state in which the upper structure 200 is fixed to the traveling device 110 and a state in which the upper structure 200 is detached from the traveling device 110 by controlling the connector 151.);
Iwasaki doesn’t clearly teach, wherein the mapping is performed using a matching and scoring algorithm utilizing multiple attributes of the vehicle signal identifier and the specification signal identifier selected from signal name, signal descriptive text, signal units, and signal data type; However, Moore [0040–0041] teaches, “As will be appreciated, there can be many different names associated with different components (and tags representing them) for an asset. As a result, it is not possible to perform a simple keyword matching process in order to identify the same tag from different virtual assets. In FIG. 3 illustrates a tag mapping process 300 in accordance with an example embodiment. In a typical tag mapping process, a user manually views two virtual models and identifies tags that are equivalent between the two virtual models. The example embodiments automate this process using machine learning, and provide recommendations for the customer (e.g., top 10 suggestions, etc.). Based on the feedback the models can be retrained. Known mappings can be used as training data to generate the models. Examples of natural language processing algorithms for tag mapping include N-grams, language modeling, classification, vector space model, latent semantic analysis, similarity scoring methods (e.g., Jaccard, Overlap, Jaro-Winkler, etc.), and latent Dirichlet allocation. Different algorithms can work better for different assets/data.”). and
an external entity receiving the signal of the vehicle from the vehicle, wherein the received signal of the vehicle is identified to the external entity by the corresponding specification signal identifier after the mapping of the received vehicle signal identifier is performed (Moore [0027]: For example, an asset can be outfitted with one or more sensors configured to monitor respective operations or conditions. Data from the sensors can be recorded or transmitted to a cloud-based or other remote computing environment.).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to incorporate the teaching of Iwasaki et al. to the Moore’s system by adding the feature of mapping. The references (Iwasaki and Moore) teach features that are analogous art and they are directed to the same field of endeavor, such as databases. Ordinary skilled artisan would have been motivated to do so to provide Iwasaki’s system with enhanced data. (See Moore [Abstract], [0023], [0040-0041], [0057]). One of the biggest advantages of network machine learning database algorithms is their ability to improve over time. Machine learning technology typically improves efficiency and accuracy thanks to the ever-increasing amounts of data that are processed.
Regarding claim 12, the system of claim 11, wherein the vehicle signal is received based on a vehicle identification that defines a plurality of extracted candidate vehicle signals (Iwasaki [0712]: FIG. 82 is a diagram showing an example of content of the vehicle allocation table d173. In the vehicle allocation table d173, a vehicle attribute ID and the number of vehicles for each vehicle are associated with the area ID and the event ID. The area ID is identification information for identifying an area. The vehicle attribute ID is identification information for identifying an attribute of the vehicle d200 participating in the event. The attribute of the vehicle is, for example, an attribute indicating a genre of a service and specific content of the service provided by the vehicle d200 or a cargo stand of the vehicle d200. The content of the attribute may be stored as a vehicle attribute table d174 in the storage d170.).
Regarding claim 13, the system of claim 11, wherein the vehicle signal is received based on a specified use case providing a schema associated with the vehicle signal that defines a plurality of extracted candidate vehicle signals (Iwasaki [1262]: Also, the display controller i281 causes the vehicle exterior display i282 to execute a display form specified by the service provider S with respect to various types of control signals. The display controller i281 may set a condition to be displayed on the vehicle exterior display i282 on the basis of the display condition data i285a.).
Regarding claim 14, the system of claim 11, the memory further caching a result of the mapping in a database stored in the memory of the vehicle or resident in the cloud for future use (Iwasaki [1056]: The output g171 includes, for example, one or more of a projector, a display, a speaker, and the like. The reproduction data differs according to the reproduction means in the vehicle g100. For example, data to be projected onto an interior surface of the vehicle 100 if the reproduction means in the vehicle g100 is a projector, data to be displayed on a display if the reproduction means in the vehicle g100 is a display, and the like are included. Also, the output g171 may reproduce the room environment using projection mapping technology.).
Regarding claim 15, the system of claim 11, based on a result of the mapping, the processor further providing the vehicle signal to the external entity labeling the vehicle signal with the specification signal identifier (Iwasaki [0030]: “According to an aspect of the present invention, there is provided a service management device including an identifier configured to identify a vehicle used by a service provider and a vehicle information provider configured to transmit information about the vehicle corresponding to the vehicle identified by the identifier to the service provider, the service management device including: a service information input configured to input service information of the service provider; and an interface controller configured to control information delivery between the vehicle and the service provider by transmitting a control signal to the vehicle on the basis of the service information.”).
Regarding claim 16, the system of claim 11, wherein the matching and scoring algorithm is operable for assessing a Levenshtein distance modified to fit signal name mapping between a signal name of the vehicle signal identifier and a signal name of the specification signal identifier (Moore [0058]: “In 740, the performing of the tag mapping may include generating a ranking for each candidate tag included in the reduced amount of tokenized tag records. The ranking may be a probability that a candidate tag is a match for the target tag. The ranking may be performed on various different algorithms (or combination of algorithms) which can be adjusted by a user. Also, different algorithms may have better accuracy for different assets, customers, use cases, and the like.”).
Regarding claim 17, the system of claim 11, wherein the matching and scoring algorithm is operable for assessing a similarity between signal descriptive text of the vehicle signal identifier and signal descriptive text of the specification signal identifier using a machine learning model trained with automotive language (Moore [0041]: “In FIG. 3 illustrates a tag mapping process 300 in accordance with an example embodiment. In a typical tag mapping process, a user manually views two virtual models and identifies tags that are equivalent between the two virtual models. The example embodiments automate this process using machine learning and provide recommendations for the customer (e.g., top 10 suggestions, etc.). Based on the feedback the models can be retrained. Known mappings can be used as training data to generate the models. Examples of natural language processing algorithms for tag mapping include N-grams, language modeling, classification, vector space model, latent semantic analysis, similarity scoring methods (e.g., Jaccard, Overlap, Jaro-Winkler, etc.), and latent Dirichlet allocation. Different algorithms can work better for different assets/data.”).
Regarding claim 18, the system of claim 11, wherein the matching and scoring algorithm is operable for assessing a similarity between signal units of the vehicle signal identifier and signal units of the specification signal identifier using a heuristic matching algorithm (Moore [0023]: “The tag may include alphanumeric characters or a grouping of words which are used to identify the component. The tag mapping process may incorporate information retrieval techniques and algorithms to narrow down a search space of potential tag matches for a target tag. Furthermore, a high fidelity algorithm may be performed to accurately determine a ranking for the remaining candidate tags, and a predetermined amount of the highest ranking candidate tags (e.g., top 10 candidate tags) may be output as possible matches for the target tag. The algorithm provides an automated process for matching together tags representing a same component from different virtual assets having different naming conventions. By grouping together tags, the tags can be analyzed together which can provide more analysis and understanding of assets.”).
Regarding claim 19, the system of claim 11, wherein the matching and scoring algorithm is operable for assessing a similarity between a signal data type of the vehicle signal identifier and a signal data type of the specification signal identifier using a data structure matching algorithm (Moore [0067]: “The processor 920 may perform a tag mapping process for one target tag in comparison to a plurality of candidate tags. As another example, the processor 920 may perform a tag mapping process for a plurality of target tags based on a plurality of candidate tags. In this later example, the processor 920 may receive a first document (e.g., target document) including a plurality of target tag records and a second document (e.g., master document) including a plurality of candidate tags.”).
Regarding claim 20, the system of claim 11, wherein the matching and scoring algorithm is operable for weighting mapping res