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 .
Application Status
Present office action is in response to preliminary amendment filed 10/06/2023. Claims 1-30 are currently pending in the application.
Claim Objections
Claim 23 is objected to because of the following informalities: Claim 23 recites “reevaluate the the electronic training content” and should be amended to recite “reevaluate the electronic training content”.
Appropriate correction is required.
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 13 is 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. In particular, in claim 13 recites “wherein the electronic training content vectors and the driver behavior data vectors” which appears to be incomplete. As a result, the metes and bounds of the claim cannot be discerned.
Examiner's Note
In view of the above noted rejection of claim 13 under 35 U.S.C. 112(b) as being incomplete, no other rejection is provided for claim 13.
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 1-12 and 14-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
In regard to independent claim 1, analyzed as representative claim:
Step 1: Statutory Category?
Independent Claim 1 recites “A method for assigning training content to a driver, comprising:”. Independent Claim 1 falls within the “process” category of 35 U.S.C. § 101.
Step 2A – Prong 1: Judicial Exception Recited?
The Independent Claim 1/Revised 2019 Guidance Table below identifies in italics the specific claim limitations found to recite an abstract idea and in bold the additional (non-abstract) claim limitations that are generic computer components.
Independent Claim 1
Revised 2019 Guidance
A method for assigning training content to a driver, comprising:
A process (method) is a statutory
subject matter class. See 35 U.S.C.
§ 101 (“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.”).
Abstract: “assigning training content to a driver …” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that a person/educator could assign training content verbally or in writing.
[L1] receiving electronic training content;
The “receiving electronic training content” implies a “computer component” which is an additional non-abstract element.
Receiving electronic training content; is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Abstract: “receiving … training content;” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could receive content visually and/or by hearing the content information.
[L2] processing the electronic training content to produce tokenized electronic training content;
The “processing the electronic training content” implies a “computer component” which is an additional non-abstract element.
Abstract: “to produce tokenized electronic training content” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could produce tokenized electronic training content by separating the text of the training data into one or more units. See published specification, ¶ 48.
[L3] processing the tokenized electronic training content to produce electronic training content vectors;
The “processing the tokenized electronic training content” implies a “computer component” which is an additional non-abstract element.
Abstract: “to produce electronic training content vectors” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could verbally and/or in writing produce content vectors.
[L4] receiving driver behavior data associated with the driver
Receiving driver behavior data associated with the driver is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Abstract: “receiving … driver behavior data associated with the driver;” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could receive driver behavior data visually and/or by hearing the driver behavior information.
[L5] processing the driver behavior data to produce tokenized driver behavior data;
Abstract: “to produce tokenized driver behavior data” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could produce tokenized driver behavior data by separating the text of the training data into one or more units. See published specification, ¶ 48.
[L6] processing the tokenized driver behavior data to produce driver behavior data vectors;
Abstract: “to produce driver behavior data vectors” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could verbally and/or in writing produce driver behavior data vectors.
[L7] evaluating the electronic training content vectors and the driver behavior data vectors to identify training content of the electronic training content relevant to the driver;
The “evaluating the electronic training content vectors and the driver behavior data vectors to identify training content of the electronic training content” implies a “computer component” which is an additional non-abstract element.
Abstract: “evaluating the electronic training content vectors and the driver behavior data vectors to identify training content relevant to the driver” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could mentally and/or using pen and paper to identify training content relevant to the driver.
[L8] generating a training assignment based on the identified training content;
Abstract: “generating a training assignment based on the identified training content” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could mentally and/or using pen and paper generate a training assignment based on the identified training content.
[L9] providing the training assignment to the driver.
Providing the training assignment to the driver is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data transmission and/or data presentation. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Abstract: “providing the training assignment to the driver” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could mentally and/or using pen and paper provide the training assignment to the driver.
The published Specification discloses “assessing the behavior data of an operator to determine a training assignment to assign to that operator” (¶ 2). It is common practice for a driving instructor to monitor a student driver’s behavior and then provide a training assignment to the student driver based of the monitored driving behavior. Thus, other than implying the “computer component” additional non-abstract limitation noted in the Independent Claim 1/Revised 2019 Guidance Table above, nothing in the claim precludes the steps from practically being performed by a human, in the mind, and/or using pen and paper. The mere implying of the “computer component” does not take the claim out of the method of organizing human activity and mental processes groupings. Accordingly, the claim recites a judicial exception (Step 2A, Prong One: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The body of the claim, as noted in the Independent Claim 1/Revised 2019 Guidance Table above, implies the “computer component” at a high level of
generality. The published Specification provides supporting exemplary descriptions of generic computer components: for example, ¶ 5: … a server, computer, computing device, or other system or components may be deployed or executed…; ¶ 27: … any type of computer-based training for any target person…; ¶ 28: environment 100 may include servers, user computers, computing devices, databases, or other systems …; ¶ 29: The environment 100 can include computing devices 140, 142, 144. The computing devices 140, 142, 144 may include general purpose personal computers (including, merely by way of example, personal computers, and/or laptop computers … the computing devices 140, 142, 144 may be any other electronic device, for example, a thin-client computer, Internet-enabled mobile telephone, and/or personal digital assistant, capable of communicating via a network 110 and/or displaying and navigating web pages or other types of electronic documents …; ¶ 30 The computing environment 100 may also include one or more servers 120. The server 120 may be a server provided in a cloud computing environment …; ¶ 31: … The server 120 may be one or more general purpose computers capable of executing programs or scripts in response to the computing devices 140, 142, 144…; ¶ 34: … The server can include any hardware, software, or hardware and/or software operable to select training for the user associated with the computing device 140, 142, 144 and provide the training to the computing device 140, 142, 144. The computing device 140, 142, 144 can be hardware, software, or a combination of hardware and software. Devices, components, systems, computers, etc. that may represent the computing device 140, 142, 144, or server 120 may be as shown and described in FIG. 3…; ¶ 35: … The external data sources 150 through 158 may be databases, servers including websites hosted on servers, another type of data store, or a combination of the data sources. Each of these external data sources 150 through 158 can be hardware, software, or a combination of hardware and software. The external data sources 150 through 158 may be computers, devices, etc., as described in conjunction with FIG. 3; ¶ 38: … the server 120 may be in a form that is ready to be processed by natural language processing, such as by natural language processor 123 shown in FIG. 2 …; ¶ 39: … The server 120 in this example includes an audio & speech recognition processor 121, training script processor 122, natural language processor 123, document analysis processor 124, training assignment processor 127, and training progress processor 128. The server 120 may include fewer or more processors and/or other components in other examples. The processors 121, 122, 123, 124, 125, 126, 127, 128 may be general purpose processors …; ¶ 46: … The natural language processor 123 performs natural language processing on textual data; ¶ 47: … The natural language processor 123 may use any natural language processing algorithm to process the data …; ¶ 72: … hardware elements may include one or more Central Processing Units (CPUs) 382; one or more input devices 384 (e.g., a mouse, a keyboard, etc.); and one or more output devices 385 (e.g., a display device, a printer, etc.). The computer system 300 may also include one or more storage devices 387. By way of example, storage device(s) 387 may be disk drives, optical storage devices, solid-state storage devices such as a Random Access Memory (“RAM”) and/or a Read-Only Memory (“ROM”), which can be programmable, flash-updateable and/or the like; ¶ 76: Examples of the CPUs 382 as described herein may include, but are not limited to …. The lack of details about the implied “computer component” indicates that it is generic, or part of generic computer elements performing generic computer-implemented steps. The claimed limitations of “receiving”, “processing to produce”, “processing to produce”, “receiving”, “processing to produce”, “processing to produce”, “evaluating to identify”, “generating”, and “providing” as recited do not purport to improve the functioning of the implied “computer component”, do not improve the technology of the technical field, and do not require a “particular machine.” Rather, they are performed using generic computer components. Further, the claim as a whole fails to effect any particular transformation of an article to a different state. The recited steps in the claim fail to provide meaningful limitations to limit the judicial exception. In this case, the claim merely implies a “computer component” as a tool to perform the abstract idea(s).
Considering the implied “computer component” of the claim both individually and as “an ordered combination” the functions performed by the computer system at each step of the process are purely conventional. Each step performed in the claim does no more than require a generic computer to perform a generic computer function. Thus, the implied “computer component” has not been shown to integrate the judicial exception into a practical application as set forth in the Revised Guidance which references the Manual of Patent Examining Procedure (“MPEP”) §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h). Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the implied “computer component” in the claim amounts to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (for example, ¶¶ 5, 27-31, 34, 35, 38, 39, 46, 47, 72, 76) describes the implied “computer component” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional elements “in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of the implied “computer component” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited data gathering (i.e., [L1] “receiving electronic training content”, [L4] “receiving driver behavior data”) and data presentation (i.e., [L9] “providing the training assignment to the driver”) are nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering and data presentation with a computer. See Elec. Power Grp., 830 F.3d at 1356 (claims to gathering, analyzing, and displaying data in real time using conventional, generic technology do not have an inventive concept); Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Hence, the additional elements are generic, well-known, and conventional computing elements. The use of the implied additional element either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to independent Claim 20:
Independent claim 20 is a system for assigning content to a driver, which falls within the “machine” category of 35 U.S.C. § 101. Independent claim 20 is a system counterpart to method claim 1, and is directed to a “server” comprising a “natural language processor”, a “document analysis processor” and a “training assignment processor” operable to perform steps comparable to those of method claim 1. The analysis set forth above is applicable to the system claim 20, with the additional observation that the use of a “server” comprising a “natural language processor”, a “document analysis processor” and a “training assignment processor” simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. (See Spec. ¶¶ 5, 28, 30, 31, 34, 35, 38, 39, 46, 47) Accordingly, independent claim 20 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 24:
Independent claim 24 is a system for assigning training content to a driver, which falls within the “machine” category of 35 U.S.C. § 101. The system for assigning training content to a driver is claimed as comprising a processor and a memory storing instructions that, when executed by the processor, cause the system to perform steps comparable to those of claim 1. As a result, independent claim 24 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 25:
Independent claim 25 is a method for assigning training content to a driver, which falls within the “process” category of 35 U.S.C. § 101. The method for assigning training content to a driver is claimed as comprising steps comparable to those of claim 1. As a result, independent claim 25 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 26:
Independent claim 25 is a method for assigning training content to a driver, which falls within the “process” category of 35 U.S.C. § 101. The method for assigning training content to a driver is claimed as comprising steps comparable to those of claim 1. As a result, independent claim 265 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 27:
Independent claim 27 is a method for assigning training content to a driver, which falls within the “process” category of 35 U.S.C. § 101. The method for assigning training content to a driver is claimed as comprising steps comparable to those of claim 1. As a result, independent claim 27 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 28:
Independent claim 28 is a method for assigning training content to a driver, which falls within the “process” category of 35 U.S.C. § 101. The method for assigning training content to a driver is claimed as comprising steps comparable to those of claim 1. As a result, independent claim 28 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 29:
Independent claim 29 is a method for assigning training content to a driver, which falls within the “process” category of 35 U.S.C. § 101. The method for assigning training content to a driver is claimed as comprising steps comparable to those of claim 1. As a result, independent claim 29 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 30:
Independent claim 30 is a method for assigning training content to a driver, which falls within the “process” category of 35 U.S.C. § 101. The method for assigning training content to a driver is claimed as comprising steps comparable to those of claim 1. As a result, independent claim 30 is rejected similarly to representative independent Claim 1.
In regard to the dependent claims:
Dependent claims 2-19 and 21-23 include all the limitations of respective independent claims 1 and 20 from which they depend and as such recite the same abstract idea(s) noted above for claims 1 and 20. None of the additional claim activities is used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-19 and 21-23 integrates the judicial exception into a practical application. While dependent claims 2-19 and 21-23 may have a narrower scope than the representative claim, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-19 and 21-23 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Claim Rejections - 35 USC § 102/103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-3, 14-15, 19-21, 24-26 and 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by or, in the alternative, under 35 U.S.C. 103 as being obvious over Baer et al. (US 20210118330 A1) (Baer).
Re claims 1, 20, 24-26 and 28:
[Claim 1] Baer discloses a method for assigning training content to a driver (at least ¶ 4: … generating coaching statements that reflect past performance …), comprising: receiving electronic training content; processing the electronic training content to produce tokenized electronic training content; processing the tokenized electronic training content to produce electronic training content vectors; receiving driver behavior data associated with the driver; processing the driver behavior data to produce tokenized driver behavior data; processing the tokenized driver behavior data to produce driver behavior data vectors (at least ¶¶ 6-9: … accessing a database of vehicle performance data of a driver's performance on a route … retrieving the drivers most recent past performance data from the database; FIG. 9 shows a flow diagram of the operations performed to generate a coaching message based on a driver's past performance …; ¶ 69: … coaching messages, provided to drivers in several fleets … have been collected and analyzed for a substantial period of time to date. Using natural language processing, each of these messages is converted into something that computers have a much easier time comparing and working on … each message is parsed into meta-data and categorized based on actual driver performance and the actual coaching message; ¶ 71: … if a coaching message last week was sent to the driver: “Another challenging week dealing with an abundance of hills and mountains! Great job pushing the boundaries on fuel efficiency!”. This prior coaching message is retrieved from the history database and first parsed into its meta-data. Then the meta-data is compared to the current driver's assigned route and schedule, climate conditions, traffic patterns etc., stored in the database; ¶ 91: the retrieved driver performance data during the previous two week periods are each parsed into performance meta-data, values and categories for each week); evaluating the electronic training content vectors and the driver behavior data vectors to identify training content of the electronic training content relevant to the driver (at least ¶ 81: For the first driver mentioned above, the meta data performance comparison is zero, hence the coaching message completely makes sense as the driver experienced everything during the week that the coaching message tasks about; i.e. a perfect match); ¶ 82: find the coaching messages that are most closely related to the actual experiences and performance measurements of the driver; ¶ 83: The vectors of meta-data for a given driver are compared to the list of meta-data vectors of coaching messages previously stored in the database based on prior driver activity. The goal is to find coaching messages where the distance between the coaching message meta data vector to the actual driver performance for that vector is zero. When this is found, that coaching message is appropriate for the driver that week); generating a training assignment based on the identified training content; and providing the training assignment to the driver (at least ¶ 99: …the identified and selected coaching statement is sent to the driver …).
Alternatively, in the event Baer is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Baer as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 20] Baer discloses a system for assigning training content to a driver (at least ¶ 4: … generating coaching statements that reflect past performance …), comprising: a server operable to: receive electronic training content; and receive driver behavior data associated with the driver, wherein the server comprises: a natural language processor operable to process the electronic training content to produce tokenized electronic training content, and process the driver behavior data to produce tokenized driver behavior data; a document analysis processor operable to process the tokenized electronic training content to produce electronic training content vectors, and process the tokenized driver behavior data to produce driver behavior data vectors (at least ¶¶ 6-9: … accessing a database of vehicle performance data of a driver's performance on a route … retrieving the drivers most recent past performance data from the database; FIG. 9 shows a flow diagram of the operations performed to generate a coaching message based on a driver's past performance …; FIG. 1 and associated text, for example, a set of one or more web server machines 104, a set of one more application server machines 106 …; ¶ 69: … coaching messages, provided to drivers in several fleets … have been collected and analyzed for a substantial period of time to date. Using natural language processing, each of these messages is converted into something that computers have a much easier time comparing and working on … each message is parsed into meta-data and categorized based on actual driver performance and the actual coaching message; ¶ 71: … if a coaching message last week was sent to the driver: “Another challenging week dealing with an abundance of hills and mountains! Great job pushing the boundaries on fuel efficiency!”. This prior coaching message is retrieved from the history database and first parsed into its meta-data. Then the meta-data is compared to the current driver's assigned route and schedule, climate conditions, traffic patterns etc., stored in the database; ¶ 91: the retrieved driver performance data during the previous two week periods are each parsed into performance meta-data, values and categories for each week); evaluating the electronic training content vectors and the driver behavior data vectors to identify training content of the electronic training content relevant to the driver (at least ¶ 81: For the first driver mentioned above, the meta data performance comparison is zero, hence the coaching message completely makes sense as the driver experienced everything during the week that the coaching message tasks about; i.e. a perfect match); ¶ 82: find the coaching messages that are most closely related to the actual experiences and performance measurements of the driver; ¶ 83: The vectors of meta-data for a given driver are compared to the list of meta-data vectors of coaching messages previously stored in the database based on prior driver activity. The goal is to find coaching messages where the distance between the coaching message meta data vector to the actual driver performance for that vector is zero. When this is found, that coaching message is appropriate for the driver that week); and generate a training assignment based on the identified training content; wherein the server is further operable to provide the training assignment to the driver (at least ¶ 99: …the identified and selected coaching statement is sent to the driver …).
Alternatively, in the event Baer is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Baer as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 24] Baer discloses a system for assigning training content to a driver (at least ¶ 4: … generating coaching statements that reflect past performance …), comprising a processor and a memory storing instructions that, when executed by the processor, cause the system to: receive electronic training content; process the electronic training content to produce tokenized electronic training content; analyze the tokenized electronic training content to produce electronic training content vectors; receive behavior data associated with the driver; process the driver behavior data to produce tokenized driver behavior data; analyze the tokenized driver behavior data to produce driver behavior data vectors (at least ¶¶ 6-9: … accessing a database of vehicle performance data of a driver's performance on a route … retrieving the drivers most recent past performance data from the database; FIG. 9 shows a flow diagram of the operations performed to generate a coaching message based on a driver's past performance …; FIG. 1 and associated text, for example, a set of one or more web server machines 104, a set of one more application server machines 106 …; ¶ 69: … coaching messages, provided to drivers in several fleets … have been collected and analyzed for a substantial period of time to date. Using natural language processing, each of these messages is converted into something that computers have a much easier time comparing and working on … each message is parsed into meta-data and categorized based on actual driver performance and the actual coaching message; ¶ 71: … if a coaching message last week was sent to the driver: “Another challenging week dealing with an abundance of hills and mountains! Great job pushing the boundaries on fuel efficiency!”. This prior coaching message is retrieved from the history database and first parsed into its meta-data. Then the meta-data is compared to the current driver's assigned route and schedule, climate conditions, traffic patterns etc., stored in the database; ¶ 91: the retrieved driver performance data during the previous two week periods are each parsed into performance meta-data, values and categories for each week); evaluating the electronic training content vectors and the driver behavior data vectors to identify training content of the electronic training content relevant to the driver (at least ¶ 81: For the first driver mentioned above, the meta data performance comparison is zero, hence the coaching message completely makes sense as the driver experienced everything during the week that the coaching message tasks about; i.e. a perfect match); ¶ 82: find the coaching messages that are most closely related to the actual experiences and performance measurements of the driver; ¶ 83: The vectors of meta-data for a given driver are compared to the list of meta-data vectors of coaching messages previously stored in the database based on prior driver activity. The goal is to find coaching messages where the distance between the coaching message meta data vector to the actual driver performance for that vector is zero. When this is found, that coaching message is appropriate for the driver that week); generating a training assignment based on the identified training content; and providing the training assignment to the driver (at least ¶ 99: …the identified and selected coaching statement is sent to the driver …).
Alternatively, in the event Baer is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Baer as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 25] Baer discloses a method for assigning training content to a driver (at least ¶ 4: … generating coaching statements that reflect past performance …), comprising: receiving electronic training content; processing the electronic training content to produce tokenized electronic training content; processing the tokenized electronic training content to produce prepared electronic training content; receiving driver behavior data associated with the driver; processing the driver behavior data to produce tokenized driver behavior data; processing the tokenized driver behavior data to produce prepared driver behavior data (at least ¶¶ 6-9: … accessing a database of vehicle performance data of a driver's performance on a route … retrieving the drivers most recent past performance data from the database; FIG. 9 shows a flow diagram of the operations performed to generate a coaching message based on a driver's past performance …; ¶ 69: … coaching messages, provided to drivers in several fleets … have been collected and analyzed for a substantial period of time to date. Using natural language processing, each of these messages is converted into something that computers have a much easier time comparing and working on … each message is parsed into meta-data and categorized based on actual driver performance and the actual coaching message; ¶ 71: … if a coaching message last week was sent to the driver: “Another challenging week dealing with an abundance of hills and mountains! Great job pushing the boundaries on fuel efficiency!”. This prior coaching message is retrieved from the history database and first parsed into its meta-data. Then the meta-data is compared to the current driver's assigned route and schedule, climate conditions, traffic patterns etc., stored in the database; ¶ 91: the retrieved driver performance data during the previous two week periods are each parsed into performance meta-data, values and categories for each week); evaluating the prepared electronic training content and the prepared driver behavior data to identify training content of the electronic training content relevant to the driver (at least ¶ 81: For the first driver mentioned above, the meta data performance comparison is zero, hence the coaching message completely makes sense as the driver experienced everything during the week that the coaching message tasks about; i.e. a perfect match); ¶ 82: find the coaching messages that are most closely related to the actual experiences and performance measurements of the driver; ¶ 83: The vectors of meta-data for a given driver are compared to the list of meta-data vectors of coaching messages previously stored in the database based on prior driver activity. The goal is to find coaching messages where the distance between the coaching message meta data vector to the actual driver performance for that vector is zero. When this is found, that coaching message is appropriate for the driver that week); generating a training assignment based on the identified training content; and providing the training assignment to the driver (at least ¶ 99: …the identified and selected coaching statement is sent to the driver …).
Alternatively, in the event Baer is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Baer as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 26] Baer discloses a method for assigning training content to a driver (at least ¶ 4: … generating coaching statements that reflect past performance …), comprising: receiving electronic training content; processing the electronic training content to produce tokenized electronic training content; processing the tokenized electronic training content to produce prepared electronic training content; receiving driver behavior data associated with the driver; processing the driver behavior data to produce tokenized driver behavior data; processing the tokenized driver behavior data to produce prepared driver behavior data (at least ¶¶ 6-9: … accessing a database of vehicle performance data of a driver's performance on a route … retrieving the drivers most recent past performance data from the database; FIG. 9 shows a flow diagram of the operations performed to generate a coaching message based on a driver's past performance …; ¶ 69: … coaching messages, provided to drivers in several fleets … have been collected and analyzed for a substantial period of time to date. Using natural language processing, each of these messages is converted into something that computers have a much easier time comparing and working on … each message is parsed into meta-data and categorized based on actual driver performance and the actual coaching message; ¶ 71: … if a coaching message last week was sent to the driver: “Another challenging week dealing with an abundance of hills and mountains! Great job pushing the boundaries on fuel efficiency!”. This prior coaching message is retrieved from the history database and first parsed into its meta-data. Then the meta-data is compared to the current driver's assigned route and schedule, climate conditions, traffic patterns etc., stored in the database; ¶ 91: the retrieved driver performance data during the previous two week periods are each parsed into performance meta-data, values and categories for each week); evaluating the prepared electronic training content and the prepared driver behavior data to identify training content of the electronic training content relevant to the driver (at least ¶ 81: For the first driver mentioned above, the meta data performance comparison is zero, hence the coaching message completely makes sense as the driver experienced everything during the week that the coaching message tasks about; i.e. a perfect match); ¶ 82: find the coaching messages that are most closely related to the actual experiences and performance measurements of the driver; ¶ 83: The vectors of meta-data for a given driver are compared to the list of meta-data vectors of coaching messages previously stored in the database based on prior driver activity. The goal is to find coaching messages where the distance between the coaching message meta data vector to the actual driver performance for that vector is zero. When this is found, that coaching message is appropriate for the driver that week); generating a training assignment based on the identified training content; and providing the training assignment to the driver (at least ¶ 99: …the identified and selected coaching statement is sent to the driver …).
Alternatively, in the event Baer is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Baer as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 28] Baer discloses a method for assigning training content to a driver (at least ¶ 4: … generating coaching statements that reflect past performance …), comprising: receiving electronic training content; processing the electronic training content document to produce tokenized electronic training; processing the tokenized electronic training content to produce electronic training content vectors; receiving a driver behavior data document associated with the driver; processing the driver behavior data document to produce a tokenized driver behavior data document; processing the tokenized driver behavior data document to produce a driver behavior data vector (at least ¶¶ 6-9: … accessing a database of vehicle performance data of a driver's performance on a route … retrieving the drivers most recent past performance data from the database; FIG. 9 shows a flow diagram of the operations performed to generate a coaching message based on a driver's past performance …; ¶ 69: … coaching messages, provided to drivers in several fleets … have been collected and analyzed for a substantial period of time to date. Using natural language processing, each of these messages is converted into something that computers have a much easier time comparing and working on … each message is parsed into meta-data and categorized based on actual driver performance and the actual coaching message; ¶ 71: … if a coaching message last week was sent to the driver: “Another challenging week dealing with an abundance of hills and mountains! Great job pushing the boundaries on fuel efficiency!”. This prior coaching message is retrieved from the history database and first parsed into its meta-data. Then the meta-data is compared to the current driver's assigned route and schedule, climate conditions, traffic patterns etc., stored in the database; ¶ 91: the retrieved driver performance data during the previous two week periods are each parsed into performance meta-data, values and categories for each week); evaluating the electronic training content vectors and the driver behavior data vector to identify training content of the electronic training content relevant to the driver (at least ¶ 81: For the first driver mentioned above, the meta data performance comparison is zero, hence the coaching message completely makes sense as the driver experienced everything during the week that the coaching message tasks about; i.e. a perfect match); ¶ 82: find the coaching messages that are most closely related to the actual experiences and performance measurements of the driver; ¶ 83: The vectors of meta-data for a given driver are compared to the list of meta-data vectors of coaching messages previously stored in the database based on prior driver activity. The goal is to find coaching messages where the distance between the coaching message meta data vector to the actual driver performance for that vector is zero. When this is found, that coaching message is appropriate for the driver that week); generating a training assignment based on the identified training content; and providing the training assignment to the driver (at least ¶ 99: …the identified