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 .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Information Disclosure Statement
All information disclosure statements were submitted prior to the first action and are incompliance with the provisions of 37 C.F.R. § 1.97. Accordingly, they have been considered.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and if all other rejections under sections 101 and 112 are overcome.
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 1-18 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) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
Independent Claims
1. A method for building a training dataset obtained at an automated vehicle, the building being performed on a server, comprising: (This is written as an intended use in the preamble.) analyzing meta information of the training dataset obtained at the automated vehicle for a requirement to extend the training dataset, wherein the analyzing is performed at the server, (The claims as a whole are directed to mental and/or mathematical processes implemented in generic computer components. Specifically, the claims are directed to the mental process of determining when to collect data about the surrounding environment. Any finding that computer components are generic also applies where the component is subsequently recited in the claims. “[A]nalyzing meta information of a training dataset obtained at the automated vehicle for a requirement to extend the training dataset wherein the analyzing is performed at the server” reads on a mental process with an instruction to implement the mental process using conventional computer components. Obtaining data is mere extra-solution activity. Use of an automated vehicle to obtain the data merely limits the data environment of the training dataset to a particular field of use, that related to autonomous driving.) and based on the requirement, sending a data capturing task to a data capturing device at the automated vehicle (The sending of an instruction to collect data based on the outcome of the mental process (the analysis resulting in the requirement) is merely part of implementing the mental process using generic computer components.) wherein the meta information is associated with datapoints (This merely limits the datapoints to a particular field of use.) and new datapoints are obtained by sending instructions from the server to the data capturing device at the automated vehicle to control the data capturing device to automatically collect the new datapoints. (Note here that “instructions from the server” are not clearly related to the “requirement.” Therefore, this limitation reads on a server merely sending a request for more data to a vehicle, and the vehicle responding by collecting the requested data points. This reads on mere data collection using conventional computer components. Obtaining new datapoints without any specific pattern and in no specific manner, other than being in response to a signal, is mere extra-solution activity. Limiting data to that collected from a vehicle merely limits the data ultimately used in training to a data environment associated with a particular field of use (i.e. the data available to an autonomous driving vehicle.))
10. A method for building a training dataset with a data capturing device, wherein the method is executed by the data capturing device at an automated vehicle and comprises: (The use of a “data capturing device at an automated vehicle” to implement the abstract ideas recited below is merely an instruction to apply an exception using generic computer components. Limiting the data capturing device to being “at an automated vehicle” merely limits the collected data to a specific data environment associated with a field of use (i.e. the field of driving)) capturing sensor data, (This reads on extra-solution activity.) determining meta information regarding the sensor data, (This reads on both a mental process and on mathematical operations and relationships. See Spec. ¶18-20 describing generating a vector used for semantic interpretation from an image.) sending the meta information to a server, - (This reads on merely transferring data, which is extra-solution activity.) receiving a transfer instruction, and sending the sensor data to the server based on the transfer instruction, (This reads on transferring data using generic computer components. This reads on extra-solution activity.) wherein the meta information is associated with datapoints of the sensor data and new datapoints are obtained by the server sending instructions to the data capturing device at the automated vehicle to control the data capturing device to automatically collect the new datapoints. (See rejection of claim 1.)
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. The following limitations of claim 1 relating to basis data input/output/transfer are WURC: “the training dataset obtained at the automated vehicle . . . sending a data capturing task to a data capturing device at the automated vehicle . . . sending instructions from the server to the data capturing device . . . automatically collect the new datapoints[.]” Similarly the following limitations of claim 10 relating to basis data input/output/transfer are WURC: “capturing sensor data . . . sending the meta information to a server . . . receiving a transfer instruction . . . sending the sensor data to the server based on the transfer instruction . . . automatically collect the new datapoints[.]” Should any other claim limitations be rejected at step 2A1 as extra-solution activity, it should be understood that such limitations are also found to be WURC at this step. This finding is based on cases which have recognized that generic input-output operations, repetitive processing operations, and storage operations are WURC.6 Other aspects of generic computing have also been found to be WURC.7 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.8 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.9 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.10 This applies to the dependent claims below.
Dependent Claims:
2. The method according to Claim 1, wherein the method further comprises an initially obtaining an item of meta information of a new datapoint from the data capturing device, (Capturing information is mere extra-solution activity. The operation of obtaining meta-information from captured data reads on a mathematical operation and on a mental process. See Spec. ¶18-20 describing generating a vector used for semantic interpretation from an image.) and wherein the data capturing task comprises an instruction to the data capturing device to send sensor data of the new datapoint to the server. (Transmitting data to the server is mere extra-solution activity, using generic computer components.)
3. The method according to Claim 2, wherein the analyzing for a requirement to extend the training dataset comprises determining a distance of the meta information of the new datapoint from meta information of datapoints of the training dataset, and the requirement to extend the training dataset is determined as a function of the distance. (Determining the (semantic) distance between data points reads on both a mental process and a mathematical operation. Determining the requirement to extend the training data set as a function of the distance reads on both a mental process and on a mathematical operation.)
4. The method according to Claim 1, wherein one of the datapoints comprises sensor data from an interior and/or exterior of the automated vehicle. (This merely limits to the particular data environment associated with a field of use, in this case sensor data acquired by a vehicle.)
5. The method according to Claim 1, wherein the meta information includes vector representations of sensor data, wherein directions in a vector space of the vector representations correspond to semantic concepts. (This merely limits to more specific mathematical relationships.)
6. The method according to Claim 1, analyzing the training dataset comprises performing a cluster analysis on the training dataset (This reads on mathematical operations and on mental processes.) in order to determine a plurality of clusters of the training dataset. (This is written as an intended use. Intended use language is explained in MPEP §§ 2103 and 2111.02. “Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure.” MPEP § 2111.04. Further, determining the training set using cluster analysis reads on both a mental process and on mathematical operations.)
7. The method according Claim 6, wherein the method further comprises determining a compensation strategy for the plurality of clusters, wherein determining the compensation strategy for the clusters comprises determining clusters which comprise less than a predetermined number of datapoints. (Determining a compensation strategy including the determination that some clusters comprise less than a predetermined number of data points reads on both a mental process and on mathematical operations.)
8. The method according to Claim 7, wherein it is determined that a cluster comprises less than a predetermined number of datapoints if the number of datapoints of the cluster is lower than a predetermined proportion of an average number of datapoints of the plurality of the clusters. (This reads on both a mental process and on mathematical operations and relationships.)
9. A server, configured to execute a method according to Claim 1. (This is a mere instruction to apply the method of claim one using generic computer components.)
11. The method according to Claim 10, wherein determining the meta information comprises imaging the sensor data in a vector space. (This reads on both a mental process and on mathematical operations.)
12. A data capturing device for use with a server, wherein the data capturing device is configured to execute the method according to Claim 10. (This is a mere instruction to apply the method of claim one using generic computer components.)
13. The data capturing device according to Claim 12, and (This merely limits using the abstract ideas to use in the field of automobiles.) the data capturing device further comprising an output device for outputting an instruction to a driver of the automated vehicle. (Data outputting is mere extra-solution activity and WURC.)
14. The data capturing device according to Claim 12, further comprising an instruction outputting device which outputs instructions to an autonomous control device of the automated vehicle. (The merely recites outputting and transmission of data which are extra-solution activity and WURC.)
15. A computer-readable storage medium which stores program code, wherein the program code comprises commands which, if the commands are executed by a processing unit, execute the method according to Claim 1. (This is a mere instruction to apply the method of claim one using generic computer components.)
16. The data capturing device according to claim 13, wherein the output device includes an audio output unit for outputting a voice output, a display and/or a device for representing a destination on a map and/or a device for representing a navigation direction. (This merely limits the outputs to fields of use (i.e. medium of communication.))
17. The data capturing device according to claim 12, wherein the data capturing device comprises the automated vehicle that is part of a vehicle fleet. (This merely limits using the abstract ideas to use in the field of fleet automobiles.)
18. The method according to claim 11, wherein the vector space is at least a ten dimensional vector space. (This alters the math and/or mental process associated with determining the meta-information, but determining the meta-information including imaging into a vector-space includes math.)
All dependent claims are rejected as containing the material of the claims from which they depend.
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 1-18 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 pre-AIA the applicant regards as the invention.
Generally: separately listed claim elements are construed as distinct components, that all claim terms must be given weight, there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims, and repeated and consistent descriptions in the specification indicate the proper scope of a claimed term. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Therefore, use of two different terms in the claims that both rely on the description of a single structure in the Specification may render at least one term indefinite because there is no way to determine which term should be construed in view of the description of the single structure.
Claim 1 recites “analyzing meta information of the training dataset obtained at the automated vehicle for a requirement to extend the training dataset, wherein the analyzing is performed at the server, and based on the requirement, sending a data capturing task to a data capturing device at the automated vehicle, wherein the meta information is associated with datapoints and new datapoints are obtained by sending instructions from the server to the data capturing device at the automated vehicle to control the data capturing device to automatically collect the new datapoints.” It is not clear whether the “data capturing task” and the separately listed “instructions from the server” sent to the data capturing device refer to distinct components or if they refer to the same component. Generally, separately listed claim elements are construed as distinct components, but the inventive concept and Applicant Remarks are more consistent with construing the claimed “capturing task” and “instructions from the server” as referring to the same component. This rejection may be overcome by using the same term if both terms are meant to refer to the same component. If separate components are the intended claim scope, citing to some portion of the description using the two terms in reference to different components may be a way forward. Note that citing to a description of instructions from a server for a data capturing task, or other descriptions of elements which are not structurally distinguishable would be inconsistent with a finding that the terms cannot reasonably be construed as the same claim element, leaving more than one reasonable interpretation of the claim language. In other words, a description of instructions to carry out the data capturing task would not support “instructions” that are separate from a “task” that is sent by a server.
Claim 10 recites “wherein the method is executed by the data capturing device at an automated vehicle and comprises: . . . receiving a transfer instruction, and sending the sensor data to the server based on the transfer instruction, wherein the meta information is associated with datapoints of the sensor data and new datapoints are obtained by the server sending instructions to the data capturing device at the automated vehicle to control the data capturing device to automatically collect the new datapoints.” Similar to the rejection of claim 1, it is not clear whether the “transfer instruction” is a different claim element from the “instructions” sent by the server. For details, see rejection above.
Claim 1 recites “A method for building a training dataset obtained at an automated vehicle, the building being performed on a server, comprising: analyzing meta information of the training dataset obtained at the automated vehicle for a requirement to extend the training dataset . . .” This appears to claim a “training dataset” that is built “on a server” and also “obtained at an automated vehicle.” Presumably, this is meant to recite a training dataset that is built on a server from raw data obtained at a vehicle. However, this is only a guess based on the inventive concept and this interpretation is somewhat inconsistent with the claim language itself. Alternatively, the claims could be directed to a training dataset which is built on a server and subsequently sent to (and thereby obtained by) an automated vehicle, but this seems inconsistent with the body of the claims and the inventive concept generally. Since there are two similarly reasonable but inconsistent interpretations the claim language is indefinite.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-2, 4, 6, 8-10, and 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (A Semantic Model for Information Sharing in Autonomous Vehicle Systems, 2017) and Shin (US 2023/0360437 English translation claiming priority to published PCT WO/JP2021/012368, filed March 2021.)
1. A method for building a training dataset obtained at an automated vehicle, the building being performed on a server, comprising: (This is written as an intended use. See also Zhang: “The main computation task in the Cloud layer is offline learning, including learning how to better predict the trajectories of objects and learning how to make better driving decisions for different environment conditions.” Zhang PP. 37-38. “We identify and organize the desired information that may impact autonomous driving and capture them in the semantic model to help with the three goals discussed above. . . . Additional semantic data that may be helpful toward the three goals are captured in the environment specification ontology and the individual object specification ontology to help with object motion prediction, driving experience data collection, and driving decision making. The vehicle sensors and actuators are modeled in the vehicle component ontology to help model the input sensor data sources and driving control parameters so as to facilitate the learning of driving methods. The semantic data related to autonomous vehicle systems are collected by the intelligent vehicles (AVs and ADAS) and roadside sensors and stored in the dynamic vehicular cloud formed by the vehicles in a region and/or uploaded to the IaaS cloud. Big data analysis and learning is done in the IaaS cloud, and the learning results will be written as functions or rules. Autonomous vehicles can download these learning results for effective object motion prediction and driving control decision making. The semantic model can also be used for normal driving decision computation.” Zhang P. 33 One of ordinary skill in the art would understand the IaaS Cloud as referring to infrastructure including servers. See also Zhang fig. 7 showing server racks labelled “IaaS Cloud.” “(LVC) uses the learning results from the cloud to help with better object trajectory prediction and driving parameter derivation for challenging maneuvers. A vehicle, before entering a region R, can obtain the recent environment parameters E(R) = < CT(R), Sit(R), Rd(R), Traf(R) > from its VC and Cloud. From Traf(R), the vehicle can know which object classes have significant presentation in R. Then, it downloads the corresponding OMPFs and VDDFs. The OMPFs should be those related to the object classes identified above. Also, for both OMPFs and VDDFs, only the rules that matches E(R) and Olist(R) (clustering based matching) of the vehicle will be downloaded and matching is performed in the Cloud.” Zhang P. 39.)) analyzing meta information of the training dataset obtained at the automated vehicle for a requirement to extend the training dataset, wherein the analyzing is performed at the server, and based on the requirement, sending a data capturing task to a data capturing device at the automated vehicle, wherein the meta information is associated with datapoints (The Specification describes “meta information” as including “vector representations of sensor data.” Spec. ¶18. While the Specification also indicates that “vector representations can in particular constitute a semantic representation” the independent claims do not recite this aspect of the disclosure. Spec. ¶19. The language “sending a data capturing task” is interpreted to include sending of captured data. The language of claim 2 indicates this is a reasonable interpretation. Zhang teaches “Intelligent vehicles upload the semantic knowledge about their driving experiences to the IaaS cloud. The Cloud analyzes the big data and learns new rules for object trajectory prediction and driving decision making.” Zhang P. 38 col. 1. One of ordinary skill in the art would understand the IaaS Cloud as referring to infrastructure including servers. See also Zhang fig. 7 showing server racks labelled “IaaS Cloud.”
The previously cited art does not expressly teach analyzing the training dataset for a requirement to extend the training dataset.
Shin teaches: “a case where the model usage device 1020 is mounted on a mobile device such as a robot or a drone, various sensors mounted on the mobile device[.]” Shin ¶256. “[0014] a training system including: a data collection device that collects data; and a training device that performs training of a machine learning model by using the data collected by the data collection device, [0015] in which the training device performs re-training of the machine learning model by using learning data that affects the training of the machine learning model to a predetermined degree or more, insufficient learning data, or data similar thereto, collected on the basis of a result of analyzing learning data that affects the training of the machine learning model.” Shin ¶¶13-16. “The training device analyzes learning data that affects the training of the machine learning model, and transmits, to the data collection device, a request signal for requesting transmission of learning data that affects the training of the machine learning model to the predetermined degree or more, the insufficient learning data, or the data similar thereto on the basis of a result of the analysis. Then, as the data collection device transmits, to the training device, data collected on the basis of the received request signal, the training device can perform re-training of the machine learning model on the basis of the data transmitted from the data collection device in response to the request signal.” Shin ¶18. “Although the accuracy in training the neural network model is improved by using a massive amount of data, it is inefficient to perform training and re-training by using data that does not contribute much to training. Therefore, in the training system 1000 according to the present disclosure, the data analysis device 1040 analyzes learning data that affects the training of the training target neural network model, and extracts, on the basis of the analysis result, meaningful learning data such as learning data that affects training of a neural network model to a predetermined degree or more, insufficient learning data, or data similar thereto. . . . [0187] The data analysis device 1040 can analyze data collected by the data collection device 1010 on the basis of, for example, a method such as explainable AI (XAI), confidence score calculation of learning data, influence function calculation, or data shortage estimation by a Bayesian deep neural network (DNN).” Shin ¶¶186-187. See also Shin ¶¶ 192, 199, 210, 247.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teaching of Shin by causing the servers of Shin to signal the vehicles used for data acquisition to capture or send more training data because collecting more data for the training device in areas that tend to have more positive effects on the model allows more improvements with less training data, thereby reducing the need for data collection storage and for computing resources. This motivation to combine applies to all subsequent citations to Shin unless otherwise indicated.) and new datapoints are obtained by sending instructions from the server to the data capturing device at the automated vehicle to control the data capturing device to automatically collect the new datapoints. (With respect to claim interpretation, note that the “instructions form the server” are not connected to the analyzing step above. The combination of art cited above teaches a server sending instructions to the automated vehicle to capture new data points. Note that all references teach ongoing processes including data collection. Zhang teaches vehicles uploading data used to train models. See e.g. Zhang P. 38 col. 1 and Fig. 7. Shin gives a drone as an example of a mobile device with a mounted sensor. Shin teaches “The training device analyzes learning data that affects the training of the machine learning model, and transmits, to the data collection device, a request signal for requesting transmission of learning data that affects the training of the machine learning model to the predetermined degree or more, the insufficient learning data, or the data similar thereto on the basis of a result of the analysis. Then, as the data collection device transmits, to the training device, data collected on the basis of the received request signal, the training device can perform re-training of the machine learning model on the basis of the data transmitted from the data collection device in response to the request signal.” Shin ¶18. The motivation to combine above applies here.)
2. The method according to Claim 1, wherein the method further comprises an initially obtaining an item of meta information of a new datapoint from the data capturing device, (See rejection of claim 1.) and wherein the data capturing task comprises an instruction to the data capturing device to send sensor data of the new datapoint to the server. (See rejection of claim 1. “The data collection device 1010 illustrated in FIG. 17 includes a sensor unit 1011, an operation input unit 1012, a control unit 1013, a log transmission unit 1014, and a data analysis unit 1015.” Shin ¶230.)
4. The method according to Claim 1, wherein one of the datapoints comprises sensor data from an interior and/or exterior of the automated vehicle. (“The data collection device 1010 illustrated in FIG. 17 includes a sensor unit 1011, an operation input unit 1012, a control unit 1013, a log transmission unit 1014, and a data analysis unit 1015.” Shin ¶230. See also rejection of claim 1.)
6. The method according to Claim 1, analyzing the training dataset comprises performing a cluster analysis on the training dataset in order to determine a plurality of clusters of the training dataset. (Zhang teaches clustering of different types of data. One of ordinary skill would understand clustering data as including operations that read on the claimed “cluster analysis.” See Zhang P.38 col 1.
9. A server, configured to execute a method according to Claim 1. (See rejection of claim 1.)
10. A method for building a training dataset with a data capturing device at an automated vehicle, (This is written as an intended use for the data capturing device. See MPEP §§ 2103, 2111.04, and 2111.02. The claimed data capturing device is addressed below. See also Zhang P. 38 col. 1.) wherein the method is executed by the data capturing device and comprises: - capturing sensor data, (“We consider five types of objects static, mobile, control, road, sensors. Objects such as tree, building, etc. are examples of static object type. Objects such as vehicle, human, bike, animal are examples of mobile objects. Sample control objects include traffic lights, stop signs, etc. Sample sensor objects include GPS, camera, LIDAR, Radar, etc. We also consider special road objects to help capture the semantics of road characteristics and conditions that can impact traffic patterns and driving decisions.” Zhang P. 35.) - determining meta information regarding the sensor data, (Zhang teaches “In this paper, we build a semantic model to capture the relevant semantic information in AV systems in addition to the existing perception model.” Zhang P. 33 col. 1. “The semantic model provides a systematic knowledge representation which describes the environment, the objects in the environment, and the situations that require attentions. It also provides a nice separation of low level sensor data processing tasks and high level semantic based driving control decision computation and learning.” Zhang P. 37.) - sending the meta information to a server, (“Intelligent vehicles upload the semantic knowledge about their driving experiences to the IaaS cloud.” Zhang P. 38 col. 1. One of ordinary skill in the art would understand the IaaS Cloud as referring to infrastructure including servers. See also Zhang fig. 7 showing server racks labelled “IaaS Cloud.”) receiving a transfer instruction, and sending the sensor data to the server based on the transfer instruction, wherein the meta information is associated with datapoints of the sensor data (Zhang teaches “Intelligent vehicles upload the semantic knowledge about their driving experiences to the IaaS cloud. The Cloud analyzes the big data and learns new rules for object trajectory prediction and driving decision making.” Zhang P. 38 col. 1. One of ordinary skill in the art would understand the IaaS Cloud as referring to infrastructure including servers. See also Zhang fig. 7 showing server racks labelled “IaaS Cloud.”
Zhang does not expressly teach a data capturing device sending sensor data to a server in response to a transfer instruction.
Shin teaches: “The training device analyzes learning data that affects the training of the machine learning model, and transmits, to the data collection device, a request signal for requesting transmission of learning data that affects the training of the machine learning model to the predetermined degree or more, the insufficient learning data, or the data similar thereto on the basis of a result of the analysis. Then, as the data collection device transmits, to the training device, data collected on the basis of the received request signal, the training device can perform re-training of the machine learning model on the basis of the data transmitted from the data collection device in response to the request signal.” Shin ¶18. See also Shin ¶¶ 192, 199, 210, 247.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Shin because sending more data in areas that tend to have more positive effects on the model allows more improvements with less training data, thereby reducing the need for data collection storage and for computing resources. This motivation to combine applies to all subsequent citations to Shin unless otherwise indicated.) and new datapoints are obtained by the server sending instructions to the data capturing device at the automated vehicle to control the data capturing device to automatically collect the new datapoints. (See rejection of claim 1.)
12. A data capturing device for use with a server, wherein the data capturing device is configured to execute the method according to Claim 10. (See rejection of claim 10.)
13. The data capturing device according to Claim 12, and the data capturing device further comprising an output device for outputting an instruction to a driver of the automated vehicle. (“In BVC, if the driver is a human, then a pseudo vision can be generated based on the integrated grid view to show the human driver the current situation on the road so that the human driver can make proper driving decisions based on the visual effects she/he is used to.” Zhang P. 39.)
14. The data capturing device according to Claim 12, further comprising an instruction outputting device which outputs instructions to an autonomous control device of the automated vehicle. (“(LVC) uses the learning results from the cloud to help with better object trajectory prediction and driving parameter derivation for challenging maneuvers. A vehicle, before entering a region R, can obtain the recent environment parameters E(R) = < CT(R), Sit(R), Rd(R), Traf(R) > from its VC and Cloud. From Traf(R), the vehicle can know which object classes have significant presentation in R. Then, it downloads the corresponding OMPFs and VDDFs. The OMPFs should be those related to the object classes identified above. Also, for both OMPFs and VDDFs, only the rules that matches E(R) and Olist(R) (clustering based matching) of the vehicle will be downloaded and matching is performed in the Cloud.” Zhang P. 39.)
15. A computer-readable storage medium which stores program code, wherein the program code comprises commands which, if the commands are executed by a processing unit, execute the method according to Claim 1. (See rejection of claim 1.)
16. The data capturing device according to claim 13, wherein the output device includes an audio output unit for outputting a voice output, a display and/or a device for representing a destination on a map and/or a device for representing a navigation direction. (“In BVC, if the driver is a human, then a pseudo vision can be generated based on the integrated grid view to show the human driver the current situation on the road so that the human driver can make proper driving decisions based on the visual effects she/he is used to.” Zhang P. 39.)
17. The data capturing device according to claim 12, wherein the data capturing device comprises the automated vehicle that is part of a vehicle fleet. (“The knowledge may be from an individual vehicle or an individual sensor or integrated after merging the knowledge from multiple vehicles. Also, the knowledge base contains historical as well as current knowledge, which can be used for learning.” Zhang P. 36. Zhang teaches “The vehicular cloud is formed dynamically by vehicles in a region. Roadside sensors in the region can also be included in the VC. The vehicles in a VC may communicate through cellular and/or wireless networks.” Zhang P. 37. “Intelligent vehicles upload the semantic knowledge about their driving experiences to the IaaS cloud. The Cloud analyzes the big data and learns new rules for object trajectory prediction and driving decision making.” Zhang P. 38 col. 1. “We consider five types of objects static, mobile, control, road, sensors. Objects such as tree, building, etc. are examples of static object type. Objects such as vehicle, human, bike, animal are examples of mobile objects. Sample control objects include traffic lights, stop signs, etc. Sample sensor objects include GPS, camera, LIDAR, Radar, etc. We also consider special road objects to help capture the semantics of road characteristics and conditions that can impact traffic patterns and driving decisions.” Zhang P. 35.)
Claim 3, 5, 7, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Shin, and Gandhe (US 10,943,583).
3. The method according to Claim 2, wherein the analyzing for a requirement to extend the training dataset comprises determining a distance of the meta information of the new datapoint from meta information of datapoints of the training dataset, and the requirement to extend the training dataset is determined as a function of the distance. (The previously cited art does not expressly teach using the distance between meta information of multiple datapoints to determine a requirement for extending the training dataset.
Gandhe teaches “If the sample utterances for the cluster are insufficient, the system may retrieve additional training data from other sources, such as text corpus(es) 180.” Gandhe col. 30 ll. 3-6. “By using a combination of the two vectors, the system may get a local as well as global representation of each document. The individual vectors for each utterance document (and thus for each set of intents I or applications S) may then be clustered into K clusters using a k-means algorithm. A distance metric d(x.sub.i,x.sub.j) may also be used to measure the cosine distance between the document vectors for clustering purposes.” Gandhe col. 27 ll. 9-15.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Gandhe because collecting more data for a cluster, that is, collecting more data within a semantic category when there is a shortage of data in that cluster/category helps train models for circumstances falling in the given category, thereby avoiding incorrect actions in a given circumstance.)
5. The method according to Claim 1, wherein the meta information includes vector representations of sensor data, wherein directions in a vector space of the vector representations correspond to semantic concepts. (The previously cited art does not expressly teach putting data into vector representations.
Gandhe teaches “As described below, the vectors may be created using a number of different techniques including word embeddings, latent semantic analysis, or other techniques. Each vector may correspond to the text for interactions with a particular speechlet or may correspond to the text for interactions with a particular intent. Thus the vectors may be application/speechlet level or intent level as described below.” Gandhe col. 4 ll. 3-9.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of as an instance of applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; The prior art contained a "base" device (method, or product) upon which the claimed invention can be seen as an "improvement” (the improvement is the use of vectors to relate similar concepts in a multidimensional space, which allows the machine to group similar concepts when training, ultimately resulting in better models.) The prior art contained a known technique that is applicable to the base device (method, or product) (as shown in the art cited above. Using vectors to represent semantically related ideas is applicable to various types of data.) One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system (one of ordinary sill in the art would have recognized that the method of Gandhe to the method/system of Zhang would have resulted in an improved system.) See MPEP § 2143(I)(D).)
7. The method according Claim 6, wherein the method further comprises determining a compensation strategy for the plurality of clusters, wherein determining the compensation strategy for the clusters comprises determining clusters which comprise less than a predermined number of datapoints. (“The training device analyzes learning data that affects the training of the machine learning model, and transmits, to the data collection device, a request signal for requesting transmission of learning data that affects the training of the machine learning model to the predetermined degree or more, the insufficient learning data, or the data similar thereto on the basis of a result of the analysis.” Shin ¶18. Data determined to be “similar” to the “insufficient learning data” teaches the claimed “cluster which comprise too low a number of data points.”
The previously cited art does not expressly teach determining clusters which comprise less than a predermined number of datapoints.
Gandhe teaches “If the sample utterances for the cluster are insufficient, the system may retrieve additional training data from other sources, such as text corpus(es) 180. That additional training data (in the form of text sentences) may then be assigned to particular clusters by creating vector representations for the sentences (for example using techniques described above), mapping those vector representations into the K clusters, and for each sentence (or paragraph or other text segment), determining the closest centroid to the vector representation of the sentence[.]” Gandhe col. 30 ll. 3-14.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Gandhe because retrieving data of a given cluster when the number is insufficient (for model training) may improve a given model.)
11. The method according to Claim 10, wherein determining the meta information comprises imaging the sensor data in a vector space. (The previously cited art does not expressly teach a high-dimensional vector.
Gandhe teaches: “To accomplish vector representations of various words, N-grams, sentences (such as those in an utterance document x), etc., the system may determine an encoded vector describing various properties of how the word, N-gram, sentence, etc. is used. In mathematical notation, given a sequence of feature data values f.sub.1, . . . f.sub.n, . . . f.sub.N, with f.sub.n being a D-dimensional vector, an encoder E(f.sub.1, . . . f.sub.N)=y projects the feature sequence to y, with y being a F-dimensional vector. F is a fixed length of the vector and is configurable depending on user of the encoded vector and other system configurations. For example, F may be between 100 and 1000 values for use in speech processing, but any size may be used.” Gandhe Col. 25 ll. 39-55.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Gandhe because encoding of features into a high dimensional space allows for efficient comparisons between features associated with different information by a model.)
18. The method according to claim 11, wherein the vector space is at least a ten dimensional vector space. (See rejection of claim 11.)
Response to Arguments
The remarks filed 12/05/2025 have been evaluated but were not fully persuasive with respect to all grounds of the rejection.
Examiner thanks applicant for the succinct remarks. While Examiner does not agree with all points, it is submitted that the claims may be amended to overcome the art of record and to include an inventive concept, as indicated in the remarks below.
Rejections under § 101:
Potentially, this application includes at least one inventive concept in the form of a technical solution. But the claims, in their current form, fail to capture this aspect of the invention.
Applicant states that the language amended into the independent claims is “not open ended because is recites a server that is sending a specific control signal” to control a data capturing device at a vehicle. It is not clear what “open ended” means in this context. Further, it is not clear how this position relates to any specific test for patent eligibility. The Remarks assert that the claimed devices and signals are not “generic” and that the claimed “features/actions do not amount to reciting a field of use.” Rem. 9. But the Remarks do not provide any arguments in support of this conclusion. See Rem. 9.
Applicant also submits that the claimed invention is directed to “meaningful technical advantages in the technical field of training data set generation” that can be used train models and thereby “improve vehicle operation.” While operations that result in specific improvements to the capabilities of autonomous driving vehicles may constitute a technical improvement, the Remarks do not indicate what “meaningful technical advantages” are provide by the claimed invention. This is especially problematic because the claims are directed to a way of choosing when to use a remote data acquisition system of an autonomous vehicle. The claims do not recite a way of acquiring data in addition to that which happens to be in the vehicle’s location. In other words, it is not clear how only collecting only some of the available data would result in improved models. The remarks do not fill in this logical gap. Further, the claims do not recite training a model with the acquired data or using a model trained with the acquired data. The omission of any specific way in which the acquired dataset results in better models and the omission of any claim language directed to training or use of the ostensibly improved models are both inconsistent with a determination that the claims are directed to a technological improvement in the form of better models leading to improved autonomous driving vehicles. It is noted that the Specification describes rerouting vehicles to acquire data that would not otherwise be acquired based on distance (i.e. semantic distance between clusters in the vector space). This would arguably offer the ability to collect an improved dataset and therefore provide the potential to improve models, ultimately offering a way of improving autonomous driving technology. But this aspect of the invention is not found in any of the claims.
In the interest of compact prosecution, Examiner notes that acquiring data at the request of a server instead of simply vacuuming up all data would more easily lend itself to a technical solution in the form of reducing burdensome bandwidth and storage requirements for autonomous vehicles used for collecting training data. Acquiring the vast amounts of data for a machine learning model onto a server from a remote device is a known technical problem and omitting redundant or similar data in a specific way may provide a technical solution. Note however, that the claims in their current form fail to connect the “requirement to extend the training data set” with the “instructions from the server . . . to . . . collect the new data points.” The result is that the claims actually do read on vacuuming up all data in response to “instruction from the server” because those instructions are not limited by the “requirement” language. Claims that read on collecting all data based on instructions from a server are clearly not directed to a specific way of reducing of bandwidth or storage requirements. However, Examiner submits that moderate claim amendments may be a way past this rejection because bandwidth reduction resulting from a specific way of selecting data (i.e. the vehicle creating and sending semantic representations of acquired data to a server, and in response, the server selectively requesting that the vehicle acquire additional related raw data via a local sensor) may provide a unique technical solution to a known technical problem. This unclaimed aspect was partially searched. No art was found teaching this concept. This is not an indication of allowable subject matter or a suggestion of specific claim language, but merely offered to point out common ground and a potential way forward. As always, overcoming the rejection under this section is dependent on specific claim language.
Rejections under § 112b:
All rejections under this section from the previous action are withdrawn in response to claim amendments. Examiner thanks applicant for making the clarifying changes.
Rejections under § 103:
The Applicant Remarks assert that claims are limited to operations performed in the order they are recited. Rem. 10. It is not clear what rule of claim construction would require this narrow interpretation. If Applicant is aware of any section in the MPEP or any case consistent with this position based on BRI or under the Phillips standard, a citation should be provided. Absent some supporting authority, the claims are not interpreted as requiring operations to be carried out based on the order that limitations appear in the claims. This is only noted to provide Applicant with a fuller understanding of Examiner’s position. Amending the claims to explicitly require operations to be performed in the order recited, would not clearly overcome the art of record. However, the section below may provide a way forward.
Applicant submits that “neither reference first obtains meta information at an automated vehicle, initially sends only the meta information to a separate server, and then the server sends a data capturing task back down to the automated vehicle.” Rem. 10. Examiner is unable to find any language in claim 1 limiting the claim scope to “an automated vehicle[] initially send[ing] only the meta information to a separate server[.]” It is submitted that Applicant’s position is inconsistent with proper construction of the language of claim 1.11 Claim 1 recites “a training dataset obtained at an automated vehicle . . . analyzing meta information of the training dataset obtained at the automated vehicle” to determine whether or not to extend the training data set by requesting further data acquisition from a data capturing device of an automated vehicle. This requires that a vehicle obtain a training dataset and that some related meta information be analyzed at the server. But the claims omit language requiring transmission of the meta information from the vehicle or to the server. Further, the claims omit language requiring the vehicle or capturing device to capture “new datapoints” in response to receiving the data capturing task. In contrast, claim 1 separately lists “a data capturing task” sent based on the requirement to extent the training data set and “instructions from the server . . . to control the data capturing device to automatically collect the new datapoints.” No relationship between the “data capturing task” and the “instructions from the server” is found in the claims. Further, since separately listed claim elements imply distinct components, it would be improper to interpret the separately listed “data capturing task” to reference the same component as “the instructions from the server” to capture new data points. Ultimately, claim 1 does not require meta information to be created on the vehicle, transmission of the meta information to the server for analysis, or connect analyzing meta information for a requirement to extent the training data to the collection of the new data points. Therefore, the claims in their current form are not non-obvious in view of the prior art, notwithstanding the potential allowability of the inventive concept in the Specification and described in the Applicant Remarks.
Conclusion:
Applicant states that Examiner is invited to contact Applicant to expedite prosecution. Applicant was contacted on 3/11/2026 at the phone number listed at the end of the Remarks. No reply was received. It is submitted that holding interviews may expedite prosecution given the specific, potentially solvable, issues in this case.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL M KNIGHT whose telephone number is (571) 272-8646. The examiner can normally be reached Monday - Friday 9-5 ET.
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, Michelle Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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PAUL M. KNIGHTExaminerArt Unit 2148
/PAUL M KNIGHT/Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC.
7 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
8 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
9 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
10 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” MPEP § 2106.05(a).
11 The Remarks do not indicate which claim is being argued. Rem. 10.