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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application Nos. 63/062,850 and 63/116,591, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Neither provisional application disposes the specific inputs used as training data for the neural network; therefore, there is no enablement present in the provisional applications for the claim limitations pertaining to the training of the neural network. Therefore, the effective filing date is the filing date of parent application 17/396,710, August 8, 2021.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-12 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6, 8, 10, 12, and 13 of U.S. Patent No. 12154387. Although the claims at issue are not identical, they are not patentably distinct from each other because all the limitations of the claims in question of the present application are present in the claims of the reference patent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4,5, 13, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 4-7, 10, 11, and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seidel et al. (US 20210366207) in view of Chini et al. (US 20170134215) in view of Wang et al. (US 20220303288) in view of Keiser et al. (US 20220092321).
Claim 1.
Seidel et al. teaches:
the trained neural network model having been trained to generate, based on collected diagnostic information, a set of one or more health metrics indicating a likelihood of failure of components of the vehicular
(Seidel – [0005]) “configured to access diagnostic data for at least one component of the motor vehicle, wherein the diagnostic data links information about at least one operating parameter of the motor vehicle with information about the at least one component, wherein the diagnostic system is configured to evaluate information about a probability of an occurrence of a fault in the motor vehicle depending on the diagnostic data and depending on the information about the at least one operating parameter”
(Seidel – [0045]) “With other preferred embodiments, diagnostic system 300 is configured to execute algorithms of artificial intelligence, AI. For this purpose, for example, at least one AI subsystem 320 can be provided, which, for example, comprises one or more artificial neural networks”
collecting, from the components of the vehicular
(Seidel – [0009]) “it is provided that the diagnostic system is configured to receive vehicle information of the motor vehicle, wherein the vehicle information comprises at least one of the following elements: … operating data characterizing an operation of at least one component of the motor vehicle, one or more fault codes characterizing a fault of at least one component of the motor vehicle.”
for a given link, inputting the collected diagnostic information, including the at least one
(Seidel – [0009]) “it is provided that the diagnostic system is configured to receive vehicle information of the motor vehicle, wherein the vehicle information comprises at least one of the following elements: … operating data characterizing an operation of at least one component of the motor vehicle, one or more fault codes characterizing a fault of at least one component of the motor vehicle.”
outputting a maintenance recommendation based on the set of health metrics generated by the trained neural network model
(Seidel – [0062]) “After the execution 330 of the diagnosis, diagnostic system 300 can transmit another optional message n7 to data processing device 100 which may contain a diagnosis result or a repair recommendation, for example.”
While Seidel et al. teaches determining a fault in a vehicle system, Seidel et al. does not explicitly teach determining a fault in a vehicle communications system; however, Chini et al. teaches:
a vehicular communication network comprising communication links… a set of one or more health metrics indicating a likelihood of failure of components of the vehicular communication network to perform in a specified manner
(Chini – [0032]) “The PHY device 206a is configured to measure high resolution echo responses received over the UTP cable 210 (and potentially other components) to perform a communication link diagnosis.”
diagnostic information including at least one loss metric with respect to a strength of signals transmitted over the communication links
(Chini – [0031]) “Additional information can be provided to indicate signalling quality, insertion loss, and return loss of the communication links used in in-vehicle networks.”
It would have been obvious to one possessing ordinary skill in the art to combine these teachings, modifying the general vehicle diagnosis system of Seidel et al. with the vehicle communications diagnosis system of Chini et al. Both Seidel et al. and Chini et al. are directed towards diagnosing faults in vehicle systems; therefore, a person of ordinary skill in the art would have recognized that the teachings could be combined with predictable results.
Seidel et al. does not explicitly teach an objective function that is a weighted sum of a cross-entropy and a mean-square error; however, Wang et al. teaches:
wherein the neural network model is trained using an objective function that is a weighted sum of a cross-entropy and a mean-square error with respect to the link health indication and the at least one loss metric
(Wang – [0017]) “the overall reconstruction loss is formulated as a weighted sum of individual loss terms for each feature, where each loss term is something specific to and appropriate for that feature (e.g., for internet proxy data: mean-squared error for numerical features, cross-entropy for categorical, etc.)”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the diagnosis system of Seidel et al. with the reconstruction loss of Wang et al. Both Seidel et al. and Wang et al. pertain to the use of neural networks to diagnose a fault; therefore, a person of ordinary skill in the art would have recognized that the teachings could be combined with predictable results. One would have been motivated to do this in order to arrive at a loss function which is appropriate for all features of the diagnosed system (Wang – [0017]).
Seidel et al. does not explicitly teach downloading the neural network to a vehicle computer; however, Keiser et al. teaches:
downloading a trained neural network model to a computer in a first vehicle
(Keiser – [0042]) “The retrained CNN 200 can be downloaded to a computing device 115 in a vehicle 110”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these two teachings, downloading the diagnostic AI of Seidel in a similar fashion to the neural network of Keiser. One would be motivated to do this because one can save time and computational expense by training one neural network and distributing it to multiple vehicles, rather than training a separate neural network for each vehicle.
Claim 2.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Seidel et al. further teaches:
wherein the neural network model is trained based on (i) collected diagnostic information from components of vehicular communication networks of a plurality of second vehicles, and (ii) collected failure information of one or more of the components of the vehicular communication networks in the plurality of second vehicles
(Seidel – [0009]) “it is provided that the diagnostic system is configured to receive vehicle information of the motor vehicle, wherein the vehicle information comprises at least one of the following elements: … operating data characterizing an operation of at least one component of the motor vehicle, one or more fault codes characterizing a fault of at least one component of the motor vehicle.”
(Seidel – [0010]) “it is provided that the diagnostic system is configured to use the vehicle information to… train or validate one or more AI subsystems of the diagnostic system.”
(Seidel – [0025]) “it is provided that the vehicle information is used to… build or supplement a database with the respective information, and/or to train or validate one or more AI subsystems of a diagnostic system. This enables a result of the diagnosis to be dependent on other vehicles for which the diagnostic data according to the vehicle information is also true.”
Claim 4.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Seidel et al. further teaches:
further comprising retraining the trained neural network model based on the collected diagnostic information from the first vehicle
(Seidel – [0009]) “it is provided that the diagnostic system is configured to receive vehicle information of the motor vehicle, wherein the vehicle information comprises at least one of the following elements: … operating data characterizing an operation of at least one component of the motor vehicle, one or more fault codes characterizing a fault of at least one component of the motor vehicle.”
(Seidel – [0010]) “it is provided that the diagnostic system is configured to use the vehicle information to… train or validate one or more AI subsystems of the diagnostic system.”
Claim 5.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Seidel et al. further teaches:
further comprising retraining the trained neural network model based on the failure information from one or both of (i) the first vehicle and (ii) the plurality of second vehicles
(Seidel – [0009]) “it is provided that the diagnostic system is configured to receive vehicle information of the motor vehicle, wherein the vehicle information comprises at least one of the following elements: … operating data characterizing an operation of at least one component of the motor vehicle, one or more fault codes characterizing a fault of at least one component of the motor vehicle.”
(Seidel – [0010]) “it is provided that the diagnostic system is configured to use the vehicle information to… train or validate one or more AI subsystems of the diagnostic system.”
Claim 6.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Chini et al. further teaches:
wherein the at least one loss metric comprises one or more parameters selected from a group of parameters including an overall insertion loss (IL), an overall return loss (RL), a near-end RL and a far-end RL
(Chini – [0031]) “Additional information can be provided to indicate signalling quality, insertion loss, and return loss of the communication links used in in-vehicle networks.”
It would have been obvious to combine these teachings for the reasons given in discussion of claim 1.
Claim 7.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Chini et al. further teaches:
wherein collecting the diagnostic information comprises collecting one or more link quality metrics for one or more of the communication links
(Chini – [0045]) “In addition, a Signal Quality Indicator (SQI) 324 can be used to provide an SCSI parameter indicative of the quality of any signal recovered by the receiver 304 over the communication link.”
It would have been obvious to combine these teachings for the reasons given in discussion of claim 1.
Claim 10.
Seidel et al. teaches:
a memory installed in a first vehicle
(Seidel – [0066]) “Data processing device 100a also comprises a computing device 120… Computing device 120 is assigned a memory device 122 which is configured to at least temporarily store a computer program PRG. Computer program PRG can be configured to execute the method, for example.”
a processor, configured to:
(Seidel – [0066]) “Data processing device 100a also comprises a computing device 120… Computing device 120 is assigned a memory device 122 which is configured to at least temporarily store a computer program PRG. Computer program PRG can be configured to execute the method, for example.”
The rest is rejected by the same rationale as claim 1.
Claim 11.
Rejected by the same rationale as claim 2.
Claim 13.
Rejected by the same rationale as claim 4.
Claim 14.
Rejected by the same rationale as claim 5.
Claim 15.
Rejected by the same rationale as claim 6.
Claim 16.
Rejected by the same rationale as claim 7.
Claim(s) 3 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. as applied to claims 1 and 10 above, and further in view of Ricci (US 20190279447).
Claim 3.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Seidel et al. does not explicitly teach collecting information from sensors; however, Ricci teaches:
wherein collecting the diagnostic information comprises collecting information from sensors associated with the components of the vehicular communication network in the first vehicle
(Ricci – [0021]) “Embodiments include a vehicle diagnostic detection and communication system comprising a vehicle control system configured to: receive sensor data from one or more vehicle sensors”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, using the sensors of Ricci to provide input to the vehicle diagnostic system of Seidel et al. Both Seidel et al. and Ricci are directed towards vehicle diagnostics systems; therefore, a person of ordinary skill in the art would have recognized that the teachings could be combined with predictable results. This is a use of a known technique (using sensors to obtain input data) to improve a similar device (vehicle diagnostic systems) in the same way.
Claim 12.
Rejected by the same rationale as claim 3.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. as applied to claims 1 and 10 above, and further in view of Cloetens (US 8639468).
Claim 8.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Seidel et al. does not explicitly teach collecting a temperature; however, Cloetens teaches:
wherein collecting the diagnostic information comprises collecting at least one temperature of at least one of the components of the vehicular communication network
(Cloetens – Abstract) “an output providing the calculated die temperature value”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the diagnostic system of Seidel et al. such that the die temperature of Cloetens is used as an input. One would have been motivated to do this because in a vehicle system, overheating of an integrated circuit may result in danger for a driver (Cloetens – Col. 1, lines 24-28).
Claim 17.
Rejected by the same rationale as claim 8.
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. as applied to claims 1 and 10 above, and further in view of Dewan (US 11353517).
Claim 9.
The combination of Seidel et al., Chini et al., Wang et al., and Keiser et al. teaches all the limitations of claim 1, as discussed above. Seidel et al. does not explicitly teach a cable fault indication; however, Dewan teaches:
further comprising outputting from the trained neural network model one or more additional outputs selected from a group of outputs including a system reliability indication, one or more warnings, a cable fault indication, a cable fault location, and an Integrated Circuit (IC) fault indication
(Dewan – Col. 10, lines 6-8) “In some embodiments, the fault comparison circuit 184 is further configured to provide an indication of a fault, based on the comparison.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the diagnostic system of Seidel et al. with the fault indication of Dewan. Both Seidel et al. and Dewan are directed towards fault detection systems; therefore, a person of ordinary skill in the art would have recognized that this combination could be made with predictable results. One would have been motivated to combine these teachings in order to notify a driver of a detected fault in a vehicle system.
Claim 18.
Rejected by the same rationale as claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH A MUELLER whose telephone number is (703)756-4722. The examiner can normally be reached M-Th 7:30-12:00, 1:00-5:30; F 8:00-12:00.
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, Navid Mehdizadeh can be reached at (571)272-7691. 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.
/S.A.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669