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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 14 recites the generic placeholders, “a screwdriving unit”, “a control unit” and “a remote data processing unit” without reciting any structure to perform the recited functional claimed limitations. Examiner looked into the specification but could not find any structural details for the above recited generic placeholders.
Claims 22 and 23 recite the generic placeholder “control unit” without reciting any structure to perform the recited functional claimed limitation.
Claim 26 recites the generic placeholders, “a screwdriving unit”, “a control unit” and “a remote data processing unit” without reciting any structure to perform the recited functional claimed limitations. Examiner looked into the specification but could not find any structural details for the above recited generic placeholders.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
For claims 14,22,23 and 26, claim limitations “a screwdriving unit”, “a control unit” and “a remote data processing unit” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification does not provide any details about components such as processor, computer or like used to perform the functional limitations for each of the above recited units. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claims so that the claim limitations will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
7. Dependent claims 15-25 and 27-30 depend on claim 14 inheriting each and every limitation of claim 14 and therefore rejected under 35 U.S.C. 112(b) for the reasons discussed above.
8. Dependent claim 31 depend on claim 26 inheriting each and every limitation of claim 26 and therefore rejected under 35 U.S.C. 112(b) for the reasons discussed above.
Claim Rejections - 35 USC § 102
9. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 14-23,26 and 28-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Abbott (US 20210240145 A1). The words used in the reference to the teach the claimed concept are different than the words recited on the claims therefore the reference anticipates the claimed invention instead of clear anticipation.
Regarding claim 14, Abott anticipates, a method for controlling an automatic screwdriving machine (automatically controlling a step bit operation of a power tool, [0003]) that comprises:
a screwdriving unit (power tool) for the automated screwing of a screw into a component (power tool having a step bit (screw) coupled to the power tool such powered screwdriver, [0003] and [0143]); and
a control unit (electronic control assembly) for controlling the screwdriving unit based on a screwdriving program that is stored in the control unit (“…The memory stores a machine learning control program that when executed by the electronic processor configures the electronic control assembly to: receive the sensor data, process, using the machine learning control program, the sensor data, and generate, using the machine learning control program, an output based on the sensor data. The output indicates step bit progress information of a step bit coupled to the power tool. The electronic control assembly controls the motor supported by the housing of the power tool based on the output…”,[0012]) and that defines the time development of a desired rotational speed at which the screw is to be driven during the screwdriving process (for the first 10 seconds of operation1, the program controls the power tool at an average rotation speed determined by the program, [0012] and [0061]),
wherein, during a screwdriving process, the control unit records a data set that relates to the screwdriving process (“…The power tool 106 includes various sensors and devices that collect usage information during the operation of the power tool 1052…”, [0049]) and that comprises the time developments of the following screwdriving parameters: an actual rotational speed of the screw (motor speed is also the speed of the bit (screw) coupled to the power tool, [0049]), a torque exerted on the screw (output torque exerted on the bit, [0058]), a feed position of the screw (position or velocity of the output shaft or like which can include feed position of the step bit, [0103]3) and a feed speed of the screw (forward feed rate of the step bit-screw, [0008] and [0103]),
wherein the control unit transmits the recorded data set to a remote data processing unit (server, [0166]) in which the screwdriving process is evaluated by means of artificial intelligence based on the transmitted data set4 (the collected data related to tool is evaluated by an external device such as server in communication with machine learning control as taught in [0167] (artificial intelligence) to determine whether the tool/screw parameters are within acceptable ranges such as tool torque within acceptable range, [0152] and [0166]).
Regarding claim 15, Abott anticipates, the method according to claim 14,
wherein the artificial intelligence is formed by a machine learning algorithm (machine learning controller of the power tool implements machine learning program having algorithms, [0052] and [0155]).
Regarding claim 16, Abott anticipates, the method according to claim 14,
wherein the artificial intelligence was trained using a plurality of heterogeneous
data sets (the training examples used to train the machine learning controller includes variable information such as type of power tool, type of bit used and others that is heterogenous data used for training, [0052], [0053] and [0072]).
Regarding claim 17, Abott anticipates, the method according to claim 14,
wherein the recorded data set is stored for a predetermined time period or also
permanently in a memory of the control unit (the memory of the electronic control assembly coupled to the power tool have data storage area for storing collected data, [0098], [0099] and [0127]).
Regarding claim 18, Abott anticipates, the method according to claim 14,
wherein the control unit transmits the recorded data set to the remote data
processing unit at the instruction of a user of the automatic screwdriving machine (when the user activates the activation switch5, the machine learning controller controls the operation of the power tool based on evaluation of the collected data performed by the server in communication with the machine learning controller, [0072], [0097] and [0101]).
Regarding claim 19, Abott anticipates, the method according to claim 14,
wherein the control unit transmits at least a first data set associated with an errorfree screwdriving process (data for performing a particular task and data used for identifying kickback error, [0106] and [0132]6) and at least a second data set associated with a faulty screwdriving process (data for identifying kickback-error and current tool data of an ongoing process, [0106], [0132] and [0148]) to the remote data processing unit at the instruction of a user of the automatic screwdriving machine (when the activation switch on the power tool is activated by the user as taught in [0101] the machine learning controller controls the tooling process automatically as taught in [0123] and evaluates the current tool progress based on training data and data collected during automatic tooling, [0146]).
Regarding claim 20, Abott anticipates, the method according to claim 19,
wherein the artificial intelligence identifies an error that occurred during the faulty
screwdriving process by evaluating the first and second data set (based on comparing current tool data (second data set) with training data threshold determined from training data such as data for particular task and data for identifying kickback (first data set), the machine learning algorithm detects anomaly during the current process such as detecting kickback, [0106], [0160]7 and [0189]).
Regarding claim 21, Abott anticipates, the method according to claim 20,
wherein the remote data processing unit transmits feedback indicating the identified error to the automatic screwdriving machine (the machine learning controller in communication with the server as taught in [0103] based on data evaluation determines an anomaly (error) and in response, instructs (feedback) the power tool to cease operation, [0189]8).
Regarding claim 22, Abott anticipates, the method according to claim 14,
wherein the control unit transmits a plurality of data sets (“A plurality of different training examples is provided to the machine learning controller 120. The machine learning controller 120 uses these training examples to generate a model (e.g., a rule, a set of equations, and the like) that helps categorize or estimate the output based on new input data…,”, [0054] that is plurality of data sets used by the machine learning controller in communication with server to train and determine process control, [0072] and [0154]) and a user-defined specification for optimizing the screwdriving process to the remote data processing unit at the instruction of a user of the automatic screwdriving machine (“In some embodiments, the machine learning control 585 includes a reinforcement learning control that allows the machine learning control 585 to continually integrate the feedback received by the user9 to optimize the performance of the machine learning control 585…”, [0168]).
Regarding claim 23, Abott anticipates, wherein the artificial intelligence creates a suggestion for at least one changed machine setting and/or for a changed screwdriving program based on the plurality of transmitted data sets and considering the transmitted optimization specification (“…In other embodiments, the server 110 sends the received usage information to the trained machine learning controller 120. The machine learning controller 120 then generates an estimated value or classification based on the input usage information10. The server electronic processor 425 then generates recommendations for future operations of the power tool 105. For example, the trained machine learning controller 120 may determine that a target step has been reached. The server electronic processor 425 may then determine that a slower motor speed for the selected operating mode may be used to prevent the step bit from exceeding the target step11. The server 110 may then transmit the suggested operating parameters to the external device 107. The external device 107 may display the suggested changes to the operating parameters and request confirmation from the user to implement the suggested changes before forwarding the changes on to the power tool 105. In other embodiments, the external device107 forwards the suggested changes to the power tool 105 and displays the suggested changes to inform the user of changes implemented by the power tool 105…”, [0060]),
said suggestion being transmitted from the remote data processing unit to the control unit of the automatic screwdriving machine in order to perform an optimized screwdriving process (“…The power tool 105 receives the updated motor speed threshold, updates the impacting mode according to the updated motor speed threshold, and operates according to the updated motor speed threshold when in the impacting mode….”, [0062] that is the changed settings is transmitted from the server to the power tool and the power tool implements the changed/updated settings during tool operation-automatic screw driving process in view of [0142]).
Regarding claim 26, Abott teaches the claimed method for controlling an automatic screwdriving machine. Therefore Abott teaches the system for controlling the screw driving machine performing all the functional limitations of the above claimed method as discussed in claim 14.
Regarding claim 28, Abott anticipates, the method according to claim 15,
wherein the machine learning algorithm comprises a decision tree algorithm, a random forest algorithm, a support vector machine algorithm or a k-nearest neighbors algorithm (“…For example, the machine learning controller 120 may implement the machine learning program using decision tree learning, associates rule learning, artificial neural networks, recurrent artificial neural networks, long short term memory neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, genetic algorithms, k-nearest neighbor (KNN), transformers, and lambda networks among others, such as those listed in Table 1 below…”, [0052]).
Regarding claim 29, Abott anticipates, the method according to claim 21,
wherein the feedback comprises a suggestion for rectifying the error12 (upon detection of anomaly, instruct the power tool to cease operation temporarily that is feedback to rectify the error, [0189]).
Regarding claim 30, Abott anticipates, the method according to claim 22,
wherein the plurality of data sets comprises a plurality of data sets associated with error-free screwdriving processes process (plurality of different training examples (data sets) is provided to the machine learning controller for training to perform a particular task, [0054] and [0106]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Abbott (US 20210240145 A1) in view of Cousineau (US 20230030253 A1).
Regarding claim 24, Abbott teaches the method according to claim 14. In addition Abbott teaches, the control unit transmits the user-defined requirements to the remote data processing unit (based on user selection for a particular task as received by the server, the server selects a program that matches the task, [0060] and [0072]),
the artificial intelligence determines a screwdriving program that matches the user-defined requirements (the server in communication with the machine learning controller/program, selects corresponding machine learning program per the user selection, [0060] and [0072]), and
the determined screwdriving program is transmitted from the remote data
processing unit to the control unit of the automatic screwdriving machine in order to perform the innovative screwdriving process (the selected machine learning program is transmitted to the power tool (screwdriver in view of [0142]) by the server, [0072]).
Abbott does not explicitly teach the details of a user of the automatic screwdriving machine defines requirements for an innovative screwdriving process and enters them into the control unit. However, Abbott explicitly teaches the user can select a particular task from the available tasks and based on user’s selection, the server determines/matches the corresponding machine learning program to be used for operation as taught in [0072]. It is not clear whether the server can match program for an innovative process like not available on the current list of particular tasks to be performed by the machine learning programs.
Cousineau teaches, a user of the automatic screwdriving machine (screw driver in view of [0142] of Abbott) defines requirements for an innovative screwdriving process and enters them into the control unit (the user specifications are translated into a custom program that can be implemented by devices to perform task such as operating a robotic arm based on certain sensor signal, [0096] and [0107]).
Cousineau and Abbott are analogous art because they are from the same field of endeavor that is implementing programs to perform certain tasks.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the matching a program for the tool based on user selection as taught by Abbott by applying the known technique of generating a custom program based on user specification that can be implemented by the tool/devices as taught by Cousineau to yield predictable results of transmitting a program to tool/devices that conforms to user specification for a task.
Regarding claim 25 combination of Abbott and Cousineau teach the method according to claim 24. In addition, Abbott teaches, wherein a data set relating to the screwdriving process is recorded during the performance of the innovative screwdriving process by means of the determined screwdriving program (based on user selection, the program selected by the server13 is transmitted to the power tool and during operation with the program, current tool usage information data is collected/recorded, [0062] and [0072]),
the recorded data set is transmitted to the data processing unit and evaluated by the artificial intelligence (the collected tool usage data is transmitted to server in communication with machine learning controller to evaluate current power tool condition and process condition such as whether to change any operational thresholds to increase efficiency of the power tool with the implemented program, [0062] and [0121]),
the artificial intelligence determines a suggestion for an optimized screwdriving program and/or an optimized machine setting on the basis of the evaluation (“…For example, the trained machine learning controller 12014 may determine that a target step has been reached. The server electronic processor 425 may then determine that a slower motor speed for the selected operating mode may be used to prevent the step bit from exceeding the target step. The server 110 may then transmit the suggested operating parameters to the external device 107. The external device 107 may display the suggested changes to the operating parameters and request confirmation from the user to implement the suggested changes before forwarding the changes on to the power tool 10515. In other embodiments, the external device107 forwards the suggested changes to the power tool 105 and displays the suggested changes to inform the user of changes implemented by the power tool 105…”, [0060]), and
the optimized screwdriving program and/or the optimized machine setting is/are transmitted from the data processing unit back to the control unit of the automatic screwdriving machine (the power tool receives the updated settings/threshold and operates the power tool (screwdriver in view of [0142]) accordingly, [0060] and [0062]).
Claim(s) 27 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Abbott (US 20210240145 A1) in view of EP51 (EP 2248951 A1).
Regarding claim 27, Abott teaches, the method according to claim 14,
wherein the screwdriving program also defines a screwdriving process (based on user target task selection, a corresponding program from the server is transmitted to the power tool which includes force values such as sustained loading for powered screw driver, [0072], [0142] and [0153]).
Abbott does not explicitly teach the details of the time development of a feed
force to be exerted on the screw during the process. However, Abbott does teaches to determine amount of sustain loading to be maintained during the tooling process in [0153]. Sustained loading can include force values to be exerted by the tool.
EP51 teaches, the time development of a feed force to be exerted on the screw during (determining axial force curve (feed force) which determines axial force to be exerted on the anchor (screw in view of Abbott) in a borehole for a period of time, page 2, 7th paragraph).
EP51 is a pertinent art to the claimed invention because it is trying to solve a same problem as the claimed invention that is determining the amount force to be exerted on the tool/anchor during a certain process. Thus, EP51 is an analogous art to Abbott since Abbott is also solving the same problem of determining tool control during a process as the claimed invention.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the automatic screw driving process performing various control on the tool using machine learning algorithm as taught by Abbott by applying the known technique of determine what axial/feed force to apply at each time step/series during a process as taught by EP51 to yield predictable results of automatically controlling the force on the screw/tool during a process.
Regarding claim 31, Abott teaches, the system according to claim 16,
wherein the screwdriving program also defines a screwdriving process (based on user target task selection, a corresponding program from the server is transmitted to the power tool which includes force values such as sustained loading for powered screw driver, [0072], [0142] and [0153]).
Abbott does not explicitly teach the details of the time development of a feed
force to be exerted on the screw during the process. However, Abbott does teaches to determine amount of sustain loading to be maintained during the tooling process in [0153]. Sustained loading can include force values to be exerted by the tool.
EP51 teaches, the time development of a feed force to be exerted on the screw during (determining axial force curve (feed force) which determines axial force to be exerted on the anchor (screw in view of Abbott) in a borehole for a period of time, page 2, 7th paragraph).
EP51 is a pertinent art to the claimed invention because it is trying to solve a same problem as the claimed invention that is determining the amount force to be exerted on the tool/anchor during a certain process. Thus, EP51 is an analogous art to Abbott since Abbott is also solving the same problem of determining tool control during a process as the claimed invention.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the automatic screw driving process performing various control on the tool using machine learning algorithm as taught by Abbott by applying the known technique of determine what axial/feed force to apply at each time step/series during a process as taught by EP51 to yield predictable results of automatically controlling the force on the screw/tool during a tooling process.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kuemmerle et al. (US 20170310810 A1) teaches an automated cooking appliance where a user can control a cooking appliance based on remote commands.
CN82 (CN107343382B) teaches robot kitchen where based on image data analysis or user command, recipes are automatically generated to be implemented by the robot. The robot can further optimize setting for greater efficiency.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tues 8:00am-12:00pm.
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, KAMINI SHAH can be reached at 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANZUMAN SHARMIN/Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 Time development.
2 Collecting screwdriving process information in view of [0143].
3 Tool movement information will also include screw/step bit/tool position, velocity and others in view of [0133].
4 See also [0174] and [0193] which recite the machine learning controller implementing machine learning (artificial intelligence) uses collected time series data to determine step bit progress with a tool-screw.
5 Instruction of a user.
6 See also [0152].
7 See also [0146].
8 See also [0111].
9 User specification. Based on user feedback and training data, the machine learning control is optimized for the tooling process.
10 Transmitted data set.
11 Suggestion for changing a setting based on transmitted data set and user feedback as taught in [0113].
12 There is no limitation defining what is the error and how the error is rectified. As such error is the anomaly and instruction for ceasing operation is the feedback for rectifying error.
13 Program created based on user specification as taught by Cousineau.
14 Artificial Intelligence.
15 Suggested optimized setting as determined by the server in communication with machine learning controller (artificial intelligence) based on evaluation using current data and training data.
Prosecution Insights