DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been presented for examination based on the application filed on 1/31/2022.
Claims 11-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claim 3-9 and 11-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US PGPUB No. US 20220055620 A1 by GASSMANN; Bernd et al.
This action is made Non-Final.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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Claim Rejections - 35 USC § 112(a) Written Description Requirement
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 11-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
MPEP 2161.01 states:
For instance, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1349-50, 94 USPQ2d at 1171 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention’s boundaries.") (citing Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1405-06); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed); Fiers v. Revel, 984 F.2d 1164, 1170, 25 USPQ2d 1601, 1606 (Fed. Cir. 1993) (rejecting the argument that "only similar language in the specification or original claims is necessary to satisfy the written description requirement").
Claim 11-18 claims using “machine learning” to predict movement. While the claim uses the machine learning model specification the specification is devoid of any disclosure of training these models so that they can be used for the specific purposes as claimed.
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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.
Claim 3-9 and 11-18 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 2 recites the following limitation:
2. The system of claim 1, wherein the memory unit includes instructions that when executed further cause the processor to determine an experienced change in displacement or change in momentum at the one or more areas of the vehicle in response to the simulated movement of the vehicle.
It is unclear what is an experienced change [emphasis added] in displacement. The claim is directed to modeling the vehicle and an item, its unclear how the experienced change factors into the model. Dependent 3-9 do not cure this deficiency and are rejected with similar rationale.
Claim 4-6 recite:
4. The system of claim 3, wherein the instructions that when executed cause the processor to combine the first and second models further cause the processor to generate a sequentially combined model.
5. The system of claim 4, wherein the instructions that when executed cause the processor to generate a sequentially combined model further cause the processor to use the output of the first model as an input to the second model, and executing the second model.
6. The system of claim 4, wherein the instructions that when executed cause the processor to generate a parallel combined model.
It is unclear what is a sequentially combined model and how it differs from the parallel combined model. If they are different, the claim 6 dependent on claim 4 appears to negating/discounting the limitations of claim 4 by performing the function in parallel. One interpretation is both models are simulated differently (e.g. on different processors --- hence parallel), when they are executed they feed to one another hence parallelly combined. The fact one is dependent on another also makes then sequential? It is unclear what interpretation should be used and how both are implemented in the same hierarchy.
Dependent claims 7 is rejected for not curing this deficiency.
Claims 11 & 12 recites:
11. The system of claim 10, wherein the movement of the item is predicted by a first machine learning model considering physical characteristics of the item.
12. The system of claim 10, wherein the movement of the vehicle is predicted by a second machine learning model considering at least one of driving characteristics of a user operating the vehicle, external conditions, and vehicle dynamics associated with the vehicle.
It is unclear (1) how the movement of the item is predicted by considering the physical characteristics of the item and (2) what aspect of machine learning is involved in predicting the movement. E.g. is machine learning predicting F=ma? For claim 11. For claim 12 how are the driving characteristics of a user operating a vehicle quantized to predict the movement of vehicle, and (3) what part machine learning plays in such prediction. Dependent claims 13-18 do not cure this deficiency and are rejected likewise.
Claim 11-18 claims using “machine learning” to predict movement. While the claim uses the machine learning model, its unclear where the machine learning model for these are derived from or trained? Specification [0098][0105][0110] have limited disclosure:
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Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US PGPUB No. US 20220055620 A1 by GASSMANN; Bernd et al.
Regarding Claim 1
Gassmann teaches A system, comprising: a processor (Gassmann: [0018]) ; and a memory unit operatively connected to the processor and including instructions that when executed cause the processor (Gassmann : [0019]) to: simulate movement of a vehicle in accordance with a driving scenario and generate a first model representative of the simulated movement of the vehicle (Gassmann: [0035] Fig.1 digital twin 110 ; Fig.2 Digital twin 224 & 234 as simulation of vehicle movement in a scenario (location, speed, acceleration, operating behavior) [0035] and its configuration affecting the movement in 234 & [0033]; This may account for operating behavior 315 in [0046] in Fig.3) ; generate a second model representative of movement of an item (Gassmann : Fig.3 [0050]"... [0050] Next, the safety scoring module 300 may, in module 330, evaluate the safety impact of the selected operating behavior on the interior objects of the digital twin to determine a safety score (e.g. criticality level) for each operating behavior. … The safety scoring module 300 may use this data to simulate (e.g., spawn simulated interior objects using a simulator to generate simulated scoring data) how the interior objects may react to the selected driving behavior and how their safety may be impacted in order to generate a safety score for the operating behavior...." ) to predict how the item will move in response to the simulated movement of the vehicle, wherein input to the second model represents physical characteristics of the item (Gassmann: [0029] "... [0029] The collected interior object data 102 may include an array of information about each interior object, including each object's basic attributes/properties such as a unique object identifier, a position of the object within the vehicle, a size of the object, an orientation of the object within the vehicle, an outline of the object (e.g., a bounding box), a shape category (e.g., a rough shape (e.g., a person-shaped object, a rectangular-shaped object, a sphere-shaped object)), a weight of the object (e.g., an estimated weight), an object classification (e.g., a person, an animal, a general object, etc.)..." Fig.2 digital twin model of interior object 214; [0044][0050]-[0051]; [0079]) ; combine the first and second models to determine predicted movement of the item in one or more areas of the vehicle in response to the simulated movement of the vehicle (Gassmann: [0051] "... In addition, safety scoring module 300 may weight the safety score by the class of object or its transitional state (e.g., the object sliding, the object falling, the object breaking, the object impacting other objects, the object is a person)....") ; perform a recommendation simulation to determined recommended placement of the item in the one or more areas of the vehicle (Gassmann: [0037] "... [0037] The safety scoring module 120 may then send generated safety scores to the vehicle for adapting the operating behavior and/or for generating warnings relevant to the safety of the interior objects...."; [0038]"... For example, the passenger message module 140 may flash a warning light, provide a message on a screen, and/or provide an audible message indicating that a safety risk exists for a particular interior object and/or that a particular object should be secured, repositioned, or otherwise adjusted to reduce the risk of injuring a passenger and/or damaging the object. ..." ) ; and output a notification reflecting the determined recommended placement of the item in the one or more areas of the vehicle (Gassmann: [0037]-[0038] Fig.1 element 140; Fig.5 element 530 [0067]-[0071]; [0078]) .
Regarding Claim 2
Gassmann teaches the system of claim 1, wherein the memory unit includes instructions that when executed further cause the processor to determine an experienced change in displacement or change in momentum at the one or more areas of the vehicle in response to the simulated movement of the vehicle (Gassmann: [0033], [0035]-[0036] – speed tracking as related momentum (mass x velocity), [0040] teaching object tracking; [0047] – teaching vehicle movement as situational data; [0050]-[0051]; [0067]) .
Regarding Claim 3
Gassmann teaches the system of claim 2, wherein the instructions that when executed cause the processor to combine the first and second models further cause the processor to determine predicted movement of the item at the one or more areas of the vehicle (Gassmann: [0036]; [0050]) .
Regarding Claim 4
Gassmann teaches the system of claim 3, wherein the instructions that when executed cause the processor to combine the first and second models further cause the processor to generate a sequentially combined model (Gassmann: [0049]-[0050]) .
Regarding Claim 5
Gassmann teaches the system of claim 4, wherein the instructions that when executed cause the processor to generate a sequentially combined model further cause the processor to use the output of the first model as an input to the second model, and executing the second model (Gassmann: Fig.3 elements 310/320 (first simulation of first digital twin models) feed into element 330 (simulation of second digital twin model of interior objects); [0051]-[0052]) .
Regarding Claim 6
Gassmann teaches the system of claim 4, wherein the instructions that when executed cause the processor to generate a parallel combined model (Gassmann: [0073] as digital twins being executed on different processing units/processors 614 and is understood as parallel processing) .
Regarding Claim 7
Gassmann teaches the system of claim 6, wherein the instructions that when executed cause the processor to generate a parallel combined model further cause the processor to execute the first and second models in parallel until convergence with the first and second models is achieved (Gassmann: [0033] interactive re-assessment of the digital twin interior model with vehicle model is considered as convergence; [0073]) .
Regarding Claim 8
Gassmann teaches the system of claim 3, wherein the instructions that when executed cause the processor to perform the recommendation simulation comprises negating predicted movement of the item at the one or more areas of the vehicle that are undesirable (Gassmann: [0066]-[0070] scenarios where negating predictive movement by recommending repositioning --- in different scenarios).
Regarding Claim 9
Gassmann teaches the system of claim 1, wherein the memory unit includes further instructions that when executed cause the processor to determine predicted item interactions based on item-related interaction information input into the combination of the first and second models at the one or more areas of the vehicle (Gassmann: [0033]; [0066]-[0070] showing item interaction in different areas of vehicle requiring repositioning) .
Regarding Claim 10
Gassmann teaches A system, comprising: a processor (Gassmann: [0018][0073]) ; and a memory unit operatively connected to the processor and including instructions that when executed cause the processor (Gassmann: [0019]) to: predict movement of an item in response to a plurality of external forces applied to the item, the plurality of external forces comprising movement of a vehicle in which the item is stored (Gassmann : Fig.3 [0050]"... [0050] Next, the safety scoring module 300 may, in module 330, evaluate the safety impact of the selected operating behavior on the interior objects of the digital twin to determine a safety score (e.g. criticality level) for each operating behavior. … The safety scoring module 300 may use this data to simulate (e.g., spawn simulated interior objects using a simulator to generate simulated scoring data) how the interior objects may react to the selected driving behavior and how their safety may be impacted in order to generate a safety score for the operating behavior...." [0029] "... [0029] The collected interior object data 102 may include an array of information about each interior object, including each object's basic attributes/properties such as a unique object identifier, a position of the object within the vehicle, a size of the object, an orientation of the object within the vehicle, an outline of the object (e.g., a bounding box), a shape category (e.g., a rough shape (e.g., a person-shaped object, a rectangular-shaped object, a sphere-shaped object)), a weight of the object (e.g., an estimated weight), an object classification (e.g., a person, an animal, a general object, etc.)..." Fig.2 digital twin model of interior object 214; [0044][0050]-[0051]; [0079])); predict movement of the vehicle in response to a plurality of driving scenarios (Gassmann: Fig. 3 elements 310, 320; [0047][0053]) ; predict the movement of the item in particular locations of the vehicle relative to each of the plurality of driving scenarios based on a prediction generated by one or more models incorporating the predicted movement of the item responsive to the plurality of external forces and the predicted movement of the vehicle responsive to the plurality of driving scenarios (Gassmann: Fig. 3 element 330 [0045]-[0053]) ; and determine whether the movement of the item in each of the particular locations is commensurate with a desired storage location for the item (Gassmann: [0032] showing various desired location like cup holder location and ) ; and upon determining that the movement of the item in one or more of the particular locations is commensurate with a desired storage location (Gassmann: [0069]"... passenger has a placed a cup of coffee on the dashboard...") , output a recommendation regarding placement of the item in the vehicle in accordance with the desired storage location for the item (Gassmann: [0069] "... safety system 500 may provided a warning message to the passenger to stow the cup in the designated cup-holder. If the passenger moves the coffee cup to the designated holder, the safety system 500 may re-analyze the interior objects of the vehicle to determine a new safety score associated with the now-secured coffee cup...") .
Regarding Claim 11
Gassmann teaches the system of claim 10, wherein the movement of the item is predicted by a first machine learning model considering physical characteristics of the item (Gassmann: [0062] "... analytics and learning module 430 may include a digital twin database 436 containing multiple instances of digital twin data that the analytics and learning module 430 may have received from the vehicle over a period time....the analytics and learning module 430 may, in module 436, use deep-learning, machine learning " ; Fig.4; first machine learning model would relate to digital twin of the interior object 214; characteristics as disclosed in [0029]-[0031]) .
Regarding Claim 12
Gassmann teaches the system of claim 10, wherein the movement of the vehicle is predicted by a second machine learning model considering at least one of driving characteristics of a user operating the vehicle, external conditions, and vehicle dynamics associated with the vehicle(Gassmann: [0062] "... analytics and learning module 430 may include a digital twin database 436 containing multiple instances of digital twin data that the analytics and learning module 430 may have received from the vehicle over a period time....the analytics and learning module 430 may, in module 436, use deep-learning, machine learning " ; The second machine learning model may be digital twin of vehicle situation data 104/224 and digital twin of vehicle configuration data 106/234 as disclosed in [0032]- [0035] at least).
Regarding Claim 13
Gassmann teaches the system of claim 12, wherein the movement of the item in the particular locations of the vehicle is predicted by a combination of the first and second machine learning models (Gassmann: [0069] [0033]) .
Regarding Claim 14
Gassmann teaches the system of claim 13, wherein the combination of the first and second machine learning models comprises a sequentially combined machine learning model (Gassmann: [0033][0049]-[0050][0062]-[0063] & Fig.4 showing machine learning and sequential nature in [0033]) .
Regarding Claim 15
Gassmann teaches the system of claim 14, wherein the memory unit includes further instructions that when executed cause the processor to use the predicted movement of the item determined by the first machine learning model as input to the second machine learning model and executing the second machine learning model (Gassmann: [0062]-[0063] & [0033]; Fig.3 elements 310/320 (first simulation of first digital twin models) feed into element 330 (simulation of second digital twin model of interior objects); [0051]-[0052]) .
Regarding Claim 16
Gassmann teaches the system of claim 13, wherein the combination of the first and second machine learning models comprises a parallel combined machine learning model (Gassmann: [0073] as digital twins being executed on different processing units/processors 614 and is understood as parallel processing; [0062]-[0063]) .
Regarding Claim 17
Gassmann teaches the system of claim 16, wherein the memory unit includes further instructions that when executed cause the processor to execute the first and second machine learning models until convergence is achieved (Gassmann: [0033] interactive re-assessment of the digital twin interior model with vehicle model is considered as convergence; [0062]-[0063], [0073]).
Regarding Claim 18
Gassmann teaches the system of claim 13, wherein the memory unit includes further instructions that when executed cause the processor to determine predicted item interactions based on item-related interaction information input into the combination of the first and second machine learning models(Gassmann: [0033] interactive re-assessment of the digital twin interior model with vehicle model e.g. [0069]"... passenger has a placed a cup of coffee on the dashboard... safety system 500 may provided a warning message to the passenger to stow the cup in the designated cup-holder. If the passenger moves the coffee cup to the designated holder, the safety system 500 may re-analyze the interior objects of the vehicle to determine a new safety score associated with the now-secured coffee cup.."; [0062]-[0063] – machine learning in view of Fig.4, [0073]).
Regarding Claim 19
Gassmann teaches the system of claim 10, wherein the memory unit includes further instructions that when executed cause the processor determine one or more of the particular locations of the vehicle at which the predicted movement of the item is undesirable (Gassmann: [0066]-[0070] scenarios where negating predictive movement by recommending repositioning --- in different scenarios).
Regarding Claim 20
Gassmann teaches the system of claim 19, wherein the instructions that when executed cause the processor to output the recommendation regarding placement of the item further cause the processor to exclude the determined one or more of the particular locations of the vehicle at which the predicted movement of the item is undesirable (Gassmann: [0066]-[0070] scenarios where negating predictive movement by recommending repositioning --- in different scenarios; E.g. in the cup on dashboard is to be excluded in placement location as undesirable; [0062]-[0063]; [0072] - processor).
Conclusion
All claims are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner’s Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Communication
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKASH SAXENA whose telephone number is (571)272-8351. The examiner can normally be reached Mon-Fri, 7AM-3:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RYAN PITARO can be reached on (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Thursday, July 24, 2025