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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/15/2025 has been entered.
Claims 1, 6 and 16 are amended. Claims 7 and 17 are canceled. Claims 1-6, 8-16 and 18-20 are presented for examination, with claims 1, 6 and 16 being independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/13/2025 and 11/05/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
Claim Objections
Applicant’s argument with regard to rejection of claims 1-6, 8-16 and 18-20 under 35 U.S.C. 101 is acknowledged. However, Examiner is not persuaded.
Claim Rejections - 35 USC § 101
Applicant’s argument with regard to rejection of claims 1-6, 8-16 and 18-20 under 35 U.S.C. 101 is acknowledged. However, Examiner is not persuaded. Based upon the consideration of claim 1 and all of the relevant factors with respect to the claim as a whole, it is directed to a judicial exception (i.e., abstract idea) without significantly more. There are no additional limitations recited beyond the judicial exception itself that integrate the exception into a practical application. More particularly, the claim does not recite: (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP §2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP §2106.05(c)); or (iv) any other meaningful limitation (see MPEP §2106.05(e)). See also Guidance, 84 FED. Reg. at 55.
The claim is broadly written. Claim 1, as an exemplary claims is directed to a system for providing streaming data to the vehicle shadow services. The claim does not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The claim fails to recite specific limitations (or a combination of limitations) that are NOT well-understood, routine, and conventional. The steps of: generate a first vehicle shadow …, generate a second vehicle shadow …; are conventional steps describe an abstract idea, they do not impose any meaningful limits on practicing the abstract idea and thus do not add significantly more to the claimed invention. In particular, the claim recites additional elements, receive streaming data …, provide the first vehicle shadow …, provide the second vehicle shadow …. However, Claim does not include any structure and/or a series of steps as to how the first/second vehicle shadow representation to a first/second user. The “one or more processors” recited in the amended claim(s), are generic computer components. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Thus, the limitation does not impose any meaningful limits on practicing the abstract idea and thus do not add significantly more to the claimed invention.
Viewed as a whole, the additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea itself. Therefore, the claim is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
See also, MPEP 2106.04(a)(2).III.C
“Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01..”
See MPEP 2111 for when and to what extent the specification can be read into claims.
For the above reasons, the Examiner maintains the rejections to claims under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 09/15/2025 have been fully considered, but are moot in light of new ground(s) of rejection. The reason are set forth below.
Applicant argues: Simoudis fails to teach “one or more hardware computing devices, comprising one or more processors, which are remote from a vehicle, configured to: receive streaming data from the vehicle … generate a first vehicle shadow representation of the vehicle … comprising a sub-set of data in the streaming data … and … generate a second vehicle shadow representation of the vehicle … comprising a different sub-set of the data in the streaming data … “
In Response: Examiner respectfully submits that: First, Simoudis discloses: The predictive model creation and management system 130 and the data orchestrator 100 are computer system/computing devices comprising processors. They are remotely and communicating through the network (e.g. The predictive model creation and management system 130 and the data orchestrator 100 are computer system. The computer system can communicate with one or more remote computer systems through the network (e.g. A predictive model creation and management system 130 may include services or applications that run in the cloud or an on-premises environment to remotely configure and manage the data orchestrator 100. The predictive model creation and management system and the data orchestrator are computer system. The computer system can communicate with one or more remote computer systems through the network. Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system, such as, for example, on the memory or electronic storage unit, Simoudis: [0061], [0066], [0168]-[0169] and Fig. 1, Fig. 4).
Second, according to Simoudis the management system 130 resides on a remote entity and remotely configure and manage the data orchestrator over a network, e.g. generating metadata corresponding to the vehicle data, that has been interpreted as generate first/second vehicle shadow. Wherein, the vehicle data comprises stream data and batch data, (e.g. the data processing module may reside on the cloud rather than the data orchestrator for processing data, Simoudis:[0008], [0010], [0011],[0116],[0152]-[0153]. (ii) process the vehicle data to generate metadata corresponding to the vehicle data [as generate a first/second vehicle shadow], wherein the vehicle data is stored in the database in a tree data structure; (iii) use at least a portion of the metadata, based at least in part on the user request, to retrieve a subset of the vehicle data from the database, which subset of the vehicle data has a size less than the vehicle data. Metadata may be generated remote from the vehicle or by a remote entity; Wherein, the vehicle data comprises at least sensor data; processing the vehicle data to generate metadata corresponding to the vehicle data, wherein the metadata includes data generated by a sensor capturing the sensor data; the vehicle data comprises stream data and batch data, Simoudis: [0018], [0019], [0024], [0026], [0108]).
The Applicant also argues that, the cited art does not teach both “generate a first vehicle shadow representation” and “generate a second vehicle shadow representation” … from a single instance/from a common of “streaming data”.
The Examiner respectfully submits that the “vehicle shadow(s)” was not defined In specification nor claim languages. For purpose of examination, the examiner has interpreted “generated metadata” according to user requested as “vehicle shadow(s)”; and Simoudis discloses at least in paragraph [0081]; multiple requests about requesting data from the same vehicle; therefore, generated metadata corresponding to data from the same vehicle for different requests have been interpreted as generate a first/second vehicle shadow from a single instance/from a common of “streaming data”, (Simoudis: [0018], [0019], [0024], [0026], [0081], [0108]).
The Applicant further argues, “A request to access vehicle data is not the same as a configuration for a vehicle shadow”; and “no indication in Simoudis that different data can be requested by the user to access”.
The Examiner respectfully submit that, since “a vehicle shadow” is not defined in claim languages; thus, the “generated metadata” corresponding to vehicle data request has been interpreted as “a vehicle shadow”. Further, Simoudis discloses that different data can be requested at least in Figs. 16 and 17, e.g. the requesting applications 1640-1 and 1640-2 or App 1, App 2 … App n , have been interpreted as different data have been requested by users.
The Examiner respectfully submits that the Applicants’ arguments only state that the cited reference(s) fail to teach or suggest limitations recited in the amended claims, but do not appear to present any clarity or submit that the limitations are fully supported by the originally-filed specification. For this reason, Examiner has full latitude to interpret each claim in the broadest reasonable sense (in re Morris, 127 F.3d 1048, 105455, 44USPQ2d 1023, 1027-28 (Fed. Cir. 1997)). Examiner references prior art using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning.
In view of at least the foregoing, the Examiner has considered Applicant's remarks. New ground of rejection is provided based on the amendment. Applicant is invited to further amendment the claims to overcome the prior arts made of record.
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 1-6, 8-16 and 18-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-6, 8-16 and 18-20 are directed to the abstract idea for providing streaming data to the vehicle shadow services. The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1: Claim 1 recites “A system …” therefore, the claim is a machines. Claim 6 recites “A method …”; the claim recites a series of steps and therefore is a process. Claim 16 recites “One or more non-transitory, computer-readable, storage media”; therefore, the claims is a manufacture.
Independent claims 1, 6 and 16
Claim 1 recites:
A system, comprising:
one or more computing devices, comprising one or more processors (generic computer components), which are remote from a vehicle, configured to:
receive (insignificant extra-solution data gathering) streaming data from the vehicle;
generate (a mental step that performs by using generic tool when the data to be generated is provided by a data gathering step) a first vehicle shadow representation of the vehicle, the first vehicle shadow representation comprising a sub-set of data included in the streaming data and related to a first view of the vehicle, wherein the first vehicle shadow representation omits a portion of the streaming data which is not relevant to the first view;
provide (insignificant extra-solution data transmission) the first vehicle shadow representation to a first user;
generate (a mental step that performs by using generic tool when the data to be generated is provided by a data gathering step) a second vehicle shadow representation of the vehicle, the second vehicle shadow representation comprising an at least partially different sub-set of the data included in the streaming data and related to a second view of the vehicle, wherein the second vehicle shadow representation omits another portion of the streaming data which is not relevant to the second view; and
provide (insignificant extra-solution data transmission) the second vehicle shadow representation to a second user.
Step 2A Prong One: The limitations of: generate a first vehicle shadow …; and generate a second vehicle shadow …; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting, one or more computing devices, one or more processors; nothing in the claim element precludes the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements: receive streaming data …, provide the first vehicle shadow …, provide the second vehicle shadow … ; the limitations amount to gathering, transmission and presentation data (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation receive streaming data …, provide the first vehicle shadow …, provide the second vehicle shadow …; are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) collecting /gathering, transmission and presentation data, Versata Dev. Group Inc....
Claim 6 recites:
A method, comprising:
receiving (insignificant extra-solution data gathering), at a vehicle shadow service implemented using one or more computing devices remote from a vehicle, wherein the one or more computing devices comprise one or more processors (generic tools), a first user selected configuration for a first vehicle shadow for the vehicle and a second user selected configuration for a second vehicle shadow for the vehicle, wherein the first user selected configuration is related to a first view of the vehicle and the second user selected configuration is related to a second view of the vehicle;
receiving (insignificant extra-solution data gathering), at the vehicle shadow service, streaming data from the vehicle;
generating (a mental step that performs by using generic tool when the data to be generated is provided by data gathering steps), via the vehicle shadow service, the first vehicle shadow for the vehicle, wherein the first vehicle shadow comprises a sub-set of data included in the streaming data in accordance with the first user selected configuration, wherein the first vehicle shadow omits a portion of the streaming data which is not relevant to the first view;
providing (insignificant extra-solution data transmission and presentation) the first vehicle shadow having the first view of the vehicle to a fist user;
generating (a mental step that performs by using generic tool when the data to be generated is provided by data gathering steps), via the vehicle shadow service, the second vehicle shadow for the vehicle, wherein the second vehicle shadow comprises an at least partially different sub-set of data included in the streaming data in accordance with the second user selected configuration, wherein the second vehicle shadow omits another portion of the streaming data which is not relevant to the second view; and
providing (insignificant extra-solution data transmission and presentation) the second vehicle shadow having the second view of the vehicle to a second user.
Step 2A Prong One: The limitations of: generating … the first vehicle shadow …, generating …the second vehicle shadow; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting, one or more computing devices, one or more processors; nothing in the claim element precludes the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements: receiving … a user selected…, receiving …streaming data …, providing …; these limitations amount to gathering, transmission and presentation data (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: receiving … a user selected…, receiving …streaming data …, providing …; are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) collecting /gathering, transmission and presentation data, Versata Dev. Group Inc....
Claim 16 recites:
One or more non-transitory, computer-readable, storage media (generic computer components) storing program instructions that, when executed on or across one or more processors, cause the one or more processors to:
receive (insignificant extra-solution data gathering) streaming data from a vehicle, wherein the one or more processors are remote from the vehicle;
generate (a mental step that performs by using generic tool when the data to be generated is provided by data gathering step) a first vehicle shadow for the vehicle, wherein the first vehicle shadow comprises a sub- set of data included in the streaming data in accordance with a first user selected configuration for the vehicle shadow related to a first view of the vehicle, wherein the first vehicle shadow omits a portion of the streaming data which is not relevant to the first view;
provide (insignificant extra-solution data transmission and presentation) the first vehicle shadow having the first view of the vehicle to a first user;
generate (a mental step that performs by using generic tool when the data to be generated is provided by data gathering steps) a second vehicle shadow for the vehicle, wherein the second vehicle shadow comprises an at least partially different sub-set of data included in the streaming data in accordance with a second user selected configuration for the second vehicle shadow related to a second view of the vehicle, wherein the second vehicle shadow omits another portion of the streaming data which is not relevant to the second view; and
provide (insignificant extra-solution data transmission and presentation) the second vehicle shadow having the second view of the vehicle to a second user.
Step 2A Prong One: The limitations of: generate … a first vehicle shadow …, generate … a second vehicle shadow …; are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting, one or more computing devices; nothing in the claim element precludes the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements: receive streaming data …, provide …; these limitations amount to gathering, transmission and presentation data (MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations: receive streaming data …, provide …; are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) collecting /gathering, transmission and presentation data, Versata Dev. Group Inc....
Since claims 1, 6 and 16 are directed to abstract ideas; thus, the claims are not patent eligible.
Claims 2-5, 7-15 and 17-20
The limitations as recited in claims 2-5, 7-15 and 17-20 are simply describe the concepts for providing streaming data to the vehicle shadow services. The claims do not include additional element(s) that is sufficient to amount to significantly more than the judicial exceptions. The claims cannot provide an inventive concept. Therefore, claims 2-5, 7-15 and 17-20 are directed to abstract ideas and are not patent eligible. Analysis of the dependent claims are shown below.
Dependent claim 2 recites the limitations, wherein the one or more computing devices are further configured to:
provide a user interface configured to receive:
a first user selection of a first set of sensor data from a first set of sensors of the vehicle to be included in the first vehicle shadow representation representing the first view of the vehicle; and
a second user selection of a second set of sensor data from a second set of sensors of the vehicle to be included in the second vehicle shadow representation representing the second view of the vehicle, wherein the first and second sets of sensors comprise at least some different sensors of the vehicle; and
generate, based on the received first and second user selections a first data model for the first vehicle shadow representation and a second data model for the second vehicle shadow representation.
The limitations are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion)
Dependent claim 3 recites the limitations, wherein at least one of the first or second data models used for a given one of the first or second vehicle shadow representations comprises a tree-structure,
wherein leaves of the tree-structure are mapped to data streams from sensors of the vehicle corresponding to the first or second sets of sensors for which sensor data is selected to be included in the first or second vehicle shadow; the limitations are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 4 recites the limitations, wherein at least one of the first or second data models used for a given one of the first or second vehicle shadow representations comprises a graph-structure,
wherein nodes of the graph-structure are mapped to data streams from sensors of the vehicle corresponding to the first or second sets of sensors for which sensor data is selected to be included in the first or second vehicle shadow, and wherein edges between the nodes indicate relationships between the selected sensors; the limitations are a processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 5 recites the limitations, wherein the user interface is further configured to receive:
a user selection of one or more operational characteristics for the first or second vehicle shadow; the limitation is a processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion), wherein supported operational characteristics available for selection comprise one or more of:
an access control list defining access privileges to one or more types of sensor data included in the first or second vehicle shadow; the limitation is a processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion)
replication rules for replicating the first or second vehicle shadow stored in a first data jurisdiction to one or more other data jurisdictions; the limitation amounts to insignificant extra-solution data analyzing (MPEP 2106.05(g));
encryption rules defining types of sensor data included in the first or second vehicle shadow that are to be encrypted; the limitation amounts to insignificant extra- data analyzing (MPEP 2106.05(g)); or
query interface rules defining a query language to be used to query the first or second vehicle shadow; the limitation amounts to insignificant extra-solution data analyzing (MPEP 2106.05(g)).
Dependent claim 8 recites the limitation, providing a user interface for the vehicle shadow service, wherein the first user selected configuration for the vehicle shadow and the second user selected configuration for the second vehicle shadow are respectively submitted to the vehicle shadow service by the first user and the second user via the user interface of the vehicle shadow service; the limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 9 recites the limitations, receiving, from the vehicle or a manufacturer of the vehicle, information indicating sensor types for sensors included in the vehicle; the limitation amounts to insignificant extra-solution data gathering (MPEP 2106.05(g), wherein the user interface comprises a listing of the sensors of the vehicle that are available for selection into a given user selected configuration for a given vehicle shadow of the vehicle; the limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claims 10-12 are similar to claims 3-5. Therefore, claims 10-12 are rejected by the same reasons as discussed in claims 3-5.
Dependent claim 13 recites the limitations, receiving a query formatted according to a graph query language, wherein the query targets data included in the sub-set of data of the streaming data included in the first vehicle shadow or the second vehicle shadow; the limitation amounts to insignificant extra-solution data analyzing (MPEP 2106.05(g)); and responding to the query based on data stored in the vehicle shadow; the limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 14 recites the limitation, identifying data of the first vehicle shadow or the second vehicle shadow targeted by the query via traversing one or more branches of a tree-structure of the vehicle shadow; the limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 15 recites the limitations, identifying data of the first vehicle shadow or the second vehicle shadow targeted by the query via traversing one or more nodes of a graph-structure of the vehicle shadow; the limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Claims 16 and 18-20 are similar to claims 1-4. Therefore, claims 16 and 18-20 are rejected by the same reasons as discussed in claims 1-4.
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.
Claims 1-6, 8-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Simoudis et al., US 2020/0364953 (hereinafter Simoudis), and further in view of Cella et al., US 2021/0272394 (hereinafter Cella).
Regarding claim 1, Simoudis discloses, A system, comprising:
one or more computing devices, comprising one or more processors, which are remote from a vehicle (e.g. A predictive model creation and management system 130 may include services or applications that run in the cloud or an on-premises environment to remotely configure and manage the data orchestrator 100. The predictive model creation and management system and the data orchestrator are computer system. The computer system can communicate with one or more remote computer systems through the network. Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system, such as, for example, on the memory or electronic storage unit, Simoudis: [0061], [0066], [0168]-[0169] and Fig. 1, Fig. 4), configured to:
receive streaming data from the vehicle (e.g. the one or more computer processors are individually or collectively programmed to (i) collect the vehicle data from the vehicle [as receive streaming data from the vehicle], the vehicle data comprises stream data and batch data, Simoudis: [0008], [0011], [0018], [0019], [0024], [0026]),
generate a first vehicle shadow representation of the vehicle, the first vehicle shadow representation comprising a sub-set of data included in the streaming data and related to a first view of the vehicle, wherein the first vehicle shadow representation omits a portion of the streaming data which is not relevant to the first view (e.g. the data processing module may reside on the cloud rather than the data orchestrator for processing data, Simoudis:[0008], [0010], [0011],[0116],[0152]-[0153]. (ii) process the vehicle data to generate metadata corresponding to the vehicle data [as generate a first/second vehicle shadow], wherein the vehicle data is stored in the database in a tree data structure; (iii) use at least a portion of the metadata, based at least in part on the user request, to retrieve a subset of the vehicle data from the database, which subset of the vehicle data has a size less than the vehicle data. Metadata may be generated remote from the vehicle or by a remote entity; Wherein, the vehicle data comprises at least sensor data; processing the vehicle data to generate metadata corresponding to the vehicle data, wherein the metadata includes data generated by a sensor capturing the sensor data; the vehicle data comprises stream data and batch data. Wherein, multiple requests about requesting data from the same vehicle; therefore, generated metadata corresponding to data from the same vehicle for different requests have been interpreted as generate a first/second vehicle shadow from a single instance/from a common of “streaming data”, Simoudis: [0018], [0019], [0024], [0026], [0081], [0108]);
generate a second vehicle shadow representation of the vehicle, the second vehicle shadow representation comprising an at least partially different sub-set of the data included in the streaming data and related to a second view of the vehicle, wherein the second vehicle shadow representation omits another portion of the streaming data which is not relevant to the second view (e.g. the data processing module may reside on the cloud rather than the data orchestrator for processing data, Simoudis:[0008], [0010], [0011],[0116],[0152]-[0153]. (ii) process the vehicle data to generate metadata corresponding to the vehicle data [as generate a first/second vehicle shadow], wherein the vehicle data is stored in the database in a tree data structure; (iii) use at least a portion of the metadata, based at least in part on the user request, to retrieve a subset of the vehicle data from the database, which subset of the vehicle data has a size less than the vehicle data. Metadata may be generated remote from the vehicle or by a remote entity; Wherein, the vehicle data comprises at least sensor data; processing the vehicle data to generate metadata corresponding to the vehicle data, wherein the metadata includes data generated by a sensor capturing the sensor data; the vehicle data comprises stream data and batch data. Wherein, multiple requests about requesting data from the same vehicle; therefore, generated metadata corresponding to data from the same vehicle for different requests have been interpreted as generate a first/second vehicle shadow from a single instance/from a common of “streaming data”, Simoudis: [0018], [0019], [0024], [0026], [0081], [0108]);
Simoudis does not directly or explicitly disclose:
provide the first/second vehicle shadow representation to a first/second user.
Cella teaches:
provide the first/second vehicle shadows representation to a first/second user (e.g. a digital twin system receiving vehicle parameter data from one or more inputs to determine the vehicle operating state; and an interface for the digital twin system to present the vehicle operating state to the manufacturer [as first/second user] of the vehicle, Cella: [0030]);
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Regarding claim 2, Simoudis and Cella in combination further discloses, wherein the one or more computing devices are further configured to: provide a user interface configured to receive (e.g. an interface for the digital twin system to present the vehicle operating state to the manufacturer of the vehicle, Cella: [0030]);
a first user selection of a first set of sensor data from a first set of sensors of the vehicle to be included in the first vehicle shadow representation representing the first view of the vehicle (e.g. metadata may allow for selection of a subset of data or a portion of the autonomous vehicle data based on the metadata. The selected portion of the vehicle data includes an aggregation of one or more of the subsets of vehicle data, Simoudis: [0017], [0152]); and
a second user selection of a second set of sensor data from a second set of sensors of the vehicle to be included in the second vehicle shadow representation representing the second view of the vehicle (e.g. metadata may allow for selection of a subset of data or a portion of the autonomous vehicle data based on the metadata. The selected portion of the vehicle data includes an aggregation of one or more of the subsets of vehicle data, Simoudis: [0017], [0152]), wherein the first and second sets of sensors comprise at least some different sensors of the vehicle (e.g. sensors can include, for example, the navigation system, sensors onboard the vehicle such as laser imaging detection and ranging (Lidar), radar, sonar, differential global positioning system (DGPS), inertial measurement unit (IMU), gyroscopes, magnetometers, accelerometers, ultrasonic sensors, image sensors (e.g., visible light, infrared), heat sensors, audio sensors, vibration sensors, conductivity sensors, chemical sensors, biological sensors, radiation sensors, conductivity sensors, proximity sensors, or any other type of sensors, Simoudis: [0062]); and
Simoudis does not directly or explicitly disclose:
generate, based on the received first and second user selections, a first data model for the first vehicle shadow representation and a second data model for the second vehicle shadow representation.
Cella teaches:
generate, based on the received first and second user selections, a first data model for the first vehicle shadow representation and a second data model for the second vehicle shadow representation (e.g. method for generating a digital twin of a vehicle includes receiving, through an interface, a request from a user of the vehicle to display state information of the vehicle; generating, using one or more processors, a digital twin representation of the vehicle based on one or more user inputs [as user selections] based on the state information of the vehicle. The user inputs for the digital twin representation include one or more of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, or a system external to the vehicle; displaying, using the interface, the state information of the vehicle using the digital twin representation of the vehicle. Interfaces may include expert system/AI system configuration interfaces 657, such as for selecting one or more models 658, selecting and configuring data sets 659 (such as sensor data, external data and other inputs described herein), Cella: [0022], [0155]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Regarding claim 3, Simoudis further discloses, wherein at least one of the first or second data models used for a given one of the first or second vehicle shadow representations comprises a tree-structure (e.g. the various predictive models may be stored using different model tree structures. A knowledge base may have different model tree structures depending on, for example, where the predictive models are being used. For example, the model tree structure for storing the predictive model used by a user experience platform may be different from the model tree structure storing the predictive model used by the data orchestrator. A tree structure may comprise one or more nodes 507 with each node including the characteristics of a predictive model and pointers to the data (e.g., training data, test data) that are used to generate the predictive model, Simoudis: [0108]-[0109], Fig. 5),
wherein leaves of the tree-structure are mapped to data streams from sensors of the vehicle corresponding to the first or second sets of sensors for which sensor data is selected to be included in the first or second vehicle shadow (e.g. the data management system may construct the database for fast and efficient data retrieval, query and delivery in comparison to using other data structures. The database may include a graph database that uses graph structures for semantic queries with nodes [as leaves of the tree structure], edges and properties to represent and store data, Simoudis: [0094]-[0095]).
Regarding claim 4, Simoudis and Cella in combination further discloses, wherein at least one of the first or second data models used for a given one of the first or second vehicle shadow representations comprises a graph-structure (e.g. the database may include a graph database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data, Simoudis: [0094]),
wherein nodes of the graph-structure are mapped to data streams from sensors of the vehicle corresponding to the first or second sets of sensors for which sensor data is selected to be included in the first or second vehicle shadow, and wherein edges between the nodes indicate relationships between the selected sensors (e.g. the shadow uses a mapped hierarchical schema of the streamed vehicle sensor data, Cella: Fig. 4, 64 and 73).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Regarding claim 5, Simoudis and Cella in combination further discloses, wherein the user interface is further configured to receive:
a user selection of one or more operational characteristics for the first or second vehicle shadow, wherein supported operational characteristics available for selection comprise one or more of:
an access control list defining access privileges to one or more types of sensor data included in the first or second vehicle shadow (e.g. The model manager may be configured to manage data flows among the various components (e.g., cloud data lake, metadata database, data orchestrator, model creator), provide precise, complex and fast queries (e.g., model query, metadata query), model deployment, maintenance, monitoring, model update, model versioning, model sharing, and various others. For example, the deployment context may be different depending on edge infrastructure and the model manager may take into account the application manifest such as edge hardware specifications, deployment location, information about compatible systems, data-access manifest for security and privacy, emulators for modeling data fields unavailable in a given deployment and version management during model deployment and maintenance. The predictive model creation and management system may have the addresses of all of the resources (i.e., applications) on the cloud listed locally in a table [as access control list] for quick lookup, Simoudis: [0127], [0142]);
replication rules for replicating the first or second vehicle shadow stored in a first data jurisdiction to one or more other data jurisdictions (e.g. the digital twin sensor data and mapping may be customized based on adjustments of the user, Cella: [0009], [0513]-[0515] Figs. 64-66);
encryption rules defining types of sensor data included in the first or second vehicle shadow that are to be encrypted (e.g. According to Simoudis, sensor data can be data transmission. Thus, a data compression method and/or encryption method may be determined for a transmission based on rules. For example, a rule may determine the compression method and/or encryption method according to a given type of data, the application that uses the data, destination of the data and the like. The rules for determining data compression method and/or encryption method may be stored in a database accessible to the data orchestrator such as the predictive models knowledge base as described above. In some cases, the rule for determining the data compression method and/or encryption method may be part of the rule for determining the data transmission. For instance, a ruleset for determining the encryption method or compression method may be called (e.g., by ruleset identifier) for determining the data transmission scheme, Simoudis: [0114]); or
query interface rules defining a query language to be used to query the first or second vehicle shadow (e.g. management system 130 may comprise a user interface (UI) module for viewing analytics, sensor data (e.g., video), or comprise a management UI for developing and deploying analytics expressions, Simoudis: [0066], [0140]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Claim 6 recites a method, comprising steps are similar to subject matter of claims 1-2. Therefore, claim 6 is rejected by the same reasons as discussed in claims 1-2.
Regarding claim 8, Cella further teaches:
providing a user interface for the vehicle shadow service, wherein the first user selected configuration for the first vehicle shadow and the second user selected configuration for the second vehicle shadow are respectively submitted to the vehicle shadow service by the first user and the second user via the user interface of the vehicle shadow service (e.g. an interface to represent to the user of the vehicle the one or more operating states of the vehicle, Cella: [0013], [0022]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Regarding claim 9, Cella further teaches:
receiving, from the vehicle or a manufacturer of the vehicle, information indicating sensor types for sensors included in the vehicle (e.g. a computer-implemented method for generating a digital twin of a vehicle includes receiving, through an interface, a request from a user of the vehicle to display state information of the vehicle, Cella: [0022]),
wherein the user interface comprises a listing of the sensors of the vehicle that are available for selection into a given user selected configuration for a given vehicle shadow of the vehicle (e.g. generating, using one or more processors, a digital twin representation of the vehicle based on one or more user inputs based on the state information of the vehicle; displaying, using the interface, the state information of the vehicle using the digital twin representation of the vehicle. In embodiments, the state information of the vehicle includes one or more of a vehicle maintenance state, a vehicle energy utilization state, a vehicle navigation state, a vehicle component state, or a vehicle driver state, Cella: [0022]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Claims 10-12 are similar to claims 3-5. Therefore, claims 10-12 are rejected by the same reasons as discussed in claims 3-5.
Regarding claim 13, Cella further teaches:
receiving a query formatted according to a graph query language, wherein the query targets data included in the sub-set of data of the streaming data included in the first vehicle shadow or the second vehicle shadow (e.g. a computer-implemented method for generating a digital twin of a vehicle includes receiving, through an interface, a request from a user of the vehicle to display state information of the vehicle, Cella: [0022]); and
responding to the query based on data stored in the vehicle shadow (e.g. generating, using one or more processors, a digital twin representation of the vehicle based on one or more user inputs based on the state information of the vehicle; displaying, using the interface, the state information of the vehicle using the digital twin representation of the vehicle. In embodiments, the state information of the vehicle includes one or more of a vehicle maintenance state, a vehicle energy utilization state, a vehicle navigation state, a vehicle component state, or a vehicle driver state, Cella: [0022]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify systems and methods for managing vehicle data as disclosed by Simoudis to include digital twin system as taught by Cella to provide basis organization of the vehicle information.
Regarding claim 14, Simoudis further teaches, wherein responding to the query comprises:
identifying data of the first vehicle shadow or the second vehicle shadow targeted by the query via traversing one or more branches of a tree-structure of the vehicle shadow (e.g. the one or more model trees 503 may be a collection of tree structures. A tree structure may comprise one or more nodes 507 with each node including the characteristics of a predictive model and pointers to the data (e.g., training data, test data) that are used/identified to generate the predictive model, Simoudis: [0108] and Fig. 5).
Regarding claim 15, Simoudis further discloses, wherein responding to the query comprises:
identifying data of the first vehicle shadow or the second vehicle shadow targeted by the query via traversing one or more nodes of a graph-structure of the vehicle shadow (e.g. the database may include a graph database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data, Simoudis: [0094]).
Claims 16, 18-20 recite, One or more non-transitory, computer-readable, storage media storing program instructions that, when executed on or across one or more processors, cause the one or more processors to performing steps are similar to subject matter of claims 2-4 and 6. Therefore, claims 16, 18-20 are rejected by the same reasons as discussed in claims 2-4 and 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CECILE H VO whose telephone number is (571)270-3031. The examiner can normally be reached Mon-Fri (9AM-5PM).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached at (571) 272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CECILE H VO/Examiner, Art Unit 2153 12/22/2025
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153