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 .
1. Claims 1-8 and 10-15 are presented for examination and claim 9 is cancelled.
Claim Rejections - 35 USC § 112
2. 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 1-8 and 10-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “similar” in claims 1, 2 and 7-8 is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term "similar," introduces ambiguity because it fails to provide a clear boundary.
The term “maximize” in claims 1, 7 and 8 is a relative term which renders the claim indefinite. The term “maximize” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The phrase "maximizes the evaluation value for productivity" describes a desired result rather than the structural or algorithmic steps required to achieve that result. It is unclear "metes and bounds" of the phrase and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
As per claim 2-6 and 10-15, these claims are at least rejected for their dependencies, directly or indirectly, on rejected claim 1. They are therefore rejected as set forth above.
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 (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.
3. 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.
3.1 Claim(s) 1-8 and 10-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nakano (WO 2021/149297 A1) in view of Ooba (US 2018/0046150 A1) further in view of MORI (US 2020/0074380 A1).
Regarding claims 1, 7 and 8, Nakano discloses process plan preparation assistance apparatus (Page 1, par. 1, a factory management device for making a factory plan), system and method, comprising:
a camera to record an image of an operator (page 7, par. 7, The worker measurement information 121 is measurement data that records the state of the worker acquired by an image sensor, a three-dimensional sensor, or the like); and
a process plan preparation assistance apparatus (page 7, par. 6, a planning unit) connected to the camera (Page 7, Par. 7, an image sensor);
production plan information acquiring circuitry (page 8, production process information) to acquire production plan information indicating a production plan for a product (page 8, par. 3-5, the production process information is information indicating a method and order of a production process such as processing and assembly of a product to be produced, a candidate of a machine (production device) to be used, a candidate of a worker, and the like. Product specification information 126 is data indicating product specifications including product design information and material information);
personnel assignment calculating circuitry (page 4, par. 9, calculate the forecast results for the productivity index) to calculate personnel assignment for an element operation corresponding to the production plan indicated by the production plan information (page 4, par. 9; page 5, par. 4, calculates the prediction result of the production index. The production process information indicates whether the process is performed by the operator alone or in collaboration with the machine and the operator) based on onsite-operator information (page 6, par. 4, par. 6, the state information of the worker is calculated and transmitted from the sensor data attached to the worker; a worker measurement for measuring a worker's movement), operation definition information (page 6, par. 6, machine measurement information that measures a predetermined value indicating the operating state of the machine that is the production equipment of the factory), and operation time information (page 6, par. 6, page 9, par. 2, machine capacity and working time),
the operation definition information indicating an element operation included in each process in a production process for the product (page 6, par. 6, machine measurement information that measures a predetermined value indicating the operating state of the machine that is the production equipment of the factory),
the operation time information indicating an element operation time taken for each operator to perform the element operation (page 4, par. 3, the operation information of the machine for each production process, the working time information, and the specification information of the product);
process plan information generating circuitry to change a production sequence (page 4, par. 10, predicting changes in production capacity, work capacity is predicted in chronological order using a trained model created by a learning method such as machine learning using manufacturing record information and product specification information. In updating the work allocation plan, the planning unit formulates a plan by using an optimization method such as a mathematical planning method) cause the personnel assignment calculating circuitry to calculate the personnel assignment (page 4, par. 9, calculate the forecast results for the productivity index); and
generate process plan information indicating personnel assignment and a production sequence to maximize an evaluation value for productivity (page 4, par. 10, the planning unit updates the work allocation in the production plan including the allocation of machines and people based on the education plan of the workers and the maintenance plan of the machines (step S1703). In this step, the planning unit 114 is a productivity index according to the future worker's work capacity and the production capacity that changes according to the change of the machine's work capacity according to the worker's education plan and the machine maintenance plan); and
operation time information updating circuitry to update the operation time information for an operator with a past operation record based (page 8, par. 5-8, a data structure of predicted worker ability, working time, the worker ability model 300 is a set of information that predicts how the working time for working the process will change in the future with respect to the specific information 1001 for each worker, product, and process. be. As a basic tendency, workers tend to have shorter working hours due to their skill, so the higher the skill level, the shorter the working time. The manufacturing record information 127 is information including the process of the product manufactured in the past, the worker, the allocation result of the machine, the working time, and the working quality) on image information indicating an image of the operator (page 7, par. 7-8, The worker measurement information 121 is measurement data that records the state of the worker acquired by an image sensor (the worker imaged), a three dimensional sensor, or the like; page 5, par. 1-3, column 7, par. 7, column 8, par. 8-10, the planning unit updates the prediction of the worker's work capacity from the result of the production plan including the worker capacity and the work allocation. Specifically, the planning unit causes the worker ability prediction unit to predict the worker's ability and updates the worker ability models. predicts the production capacity from the work capacity of the worker and the machine and calculates the prediction result of the productivity index. The production process- information indicates whether the process is performed by the operator alone or in collaboration with the machine and the operator. A product in which the manufacturing performance tends to be high when the movement amount of a predetermined part is reduced and the movement speed is high, if the measurement information of the worker has the same tendency, the worker ability prediction The unit 111 highly predicts the manufacturing results and highly predicts the worker ability); and
generate the operation time information for the operator with no past operation record (page 4, par.10, Replan worker and machine work assignments and update work plans to optimize. In predicting changes in production capacity, work capacity is predicted in chronological order using a trained model created by a learning method such as machine learning using manufacturing record information 127 and product specification information 126. In updating the work allocation plan, factory production capacity prediction process, the production capacity can be predicted by using the capacity of the worker).
Nakano fails to disclose the onsite-operator information indicating an operator being onsite.
However, Ooba discloses the onsite-operator information indicating an operator being onsite (Fig. 1, [0019], a first sensor 1a detects data on the operator A, while a second sensor 1b detects data on the operator and notifies the operation monitoring unit).
Nakano and Ooba are analogous art. They relate to operation management systems. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify monitoring the motion amount and condition amount of the operator and managing information from the sensor, taught by Ooba, incorporated with measuring a worker's movement and factory management method, taught by Nakano, in order to easily identifying the worker ability and assigned the work accordingly.
The combination of Nakano and Ooba fail to disclose predict an element operation time of an operator with no past operation record based on the operation time information for an operator with a past operation record who has an attribute most similar to an attribute of the operator with no past operation record.
However, Mori discloses predict an element operation time of an operator with no past operation record based on the operation time information for an operator with a past operation record who has an attribute most similar to an attribute of the operator with no past operation record (Abstract, a skill-level calculation unit configured to calculate the skill level of the target operator with respect to the work step, the skill level indicating a degree on a spectrum of whether the target operator can suitably accomplish the work step; a second acquisition unit configured to acquire movement data of a model operator at a skill level slightly higher than or equal to the skill level calculated for the target operator; and an instruction determination unit configured to compare the acquired movement data for the model operator with the acquired movement data of the target operator and determine an instruction that allows the movement of the target operator to approach the movement of the model operator on the basis of the results of comparison).
Mori, Nakano and Ooba are analogous art. They relate to operation management systems. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify monitoring the motion amount and condition amount of the operator and managing information from the sensor, taught by Mori, incorporated with teaching of Nakano and Ooba, stated obove, in order to evaluating the skill level of a target operator with respect to a work step and for allowing said target operator to learn the work efficiently.
Regarding claim 2, The combination of Ooba, Mori and Nakano disclose:
Ooba discloses the operation time information updating circuitry ([0023], the operator monitor unit 4 preferably monitors at least one of the operation time) includes detecting circuitry to detect, from the image indicated by the image information, an operator ([0022]-[0023], information from the sensor (1a, 1b) received by the sensor management unit 3. The “motion amount” of the operator refers to information to which, for example, the body motion of the operator who is performing the specific operation on the work is quantified. The “condition amount” of the operator refers to information to which, for example, the physical condition, the mental condition, the degree of concentration at the operation, or the like of the operator, which is estimated from the facial expression of the operator, is quantified), and a type of a product handled by the operator ([0019], the operators A and B perform operation on the workpieces 31 to 34 conveyed on a conveyor 40. However, the operation is not limited thereto, and operators A and B may perform other operations.
operation time measuring circuitry ([0024], the operation time to measure an element operation time of the operator based on the image information (sensor measuring unit 3 the operator monitor unit 4 obtains information on an operation time, the number of defective workpieces a difference in an operation amount, a motion amount, and the like from the sensor management unit 3),
updating circuitry to update (Fig. 6, update table value), when the operator detected by the detecting circuitry has a past operation record (Fig. 7, [0020], detects the body motion, a change in the posture, the facial expression, and the like of the operator. The sensor preferably has the function of measuring an operation time from the start to the end of the sequential operation, which is repeatedly performed by the operator),
the operation time information of the operator [0032], operation time from the start to the end of sequential operation repeatedly performed by the operator) with the past operation record based on the operator ([0025], Upon receiving an operation details change notification from the host management unit, the notification management unit, transfers the operation details change notification to at least one of the operators (A and B). Upon receiving a condition notification request from at least one of the operators) and the type of the product handled by the operator detected by the detecting circuitry and the element operation time measured by the operation time measuring circuitry (Fig. 1, [0020], [0023], [0025], the plurality of operators are present, the sensor (1a, 1b) preferably has the function of measuring the difference in an operation amount between the operators. The sensor (1a, 1b) also preferably has the function of measuring the motion amount of the operation), and
predict an element operation time of the operator with no past operation record based on the operation time information of the identified operator ([0023] The operator monitor unit 4 monitors at least one of the motion amount and the condition amount of the operator (A, B) included in the information from the sensor (1a, 1b) received by the sensor management unit 3. The “motion amount” of the operator refers to information to which, for example, the body motion of the operator who is performing the specific operation on the work is quantified. The “condition amount” of the operator refers to information to which, for example, the physical condition, the mental condition, the degree of concentration at the operation, or the like of the operator, which is estimated from the facial expression of the operator, is quantified. The operator monitors unit 4 monitor an operation time from the start to the end of the sequential operation, which is repeatedly performed by at least one operator (A, B). The operator monitors unit monitor the degree of accomplishment of the operation performed by the operator (A, B). The operator monitors unit 4 monitor the number of defective workpieces produced by the operation performed by the operator (A, B). When the plurality of operators is present, the operator monitor unit 4 may monitor the difference in the operation amount between the operators), and
predict an element operation time of the operator with no past operation record based on the operation time information of the identified operator ([0020], The sensor (1a, 1b) preferably has the function of counting the number of defective workpieces produced by the operation performed by the operator. When the plurality of operators is present, the sensor (1a, 1b) preferably has the function of measuring the difference in an operation amount between the operators. The sensor (1a, 1b) also preferably has the function of measuring the motion amount of the operator. [0040], [0067], As the degree of accomplishment of operation performed by the operator (A or B), there is, for example, the ratio between the number of operated workpieces and the target number of workpieces to be operated by the operator), and the operator monitor unit 4 obtains data on an operation time, the number of defective workpieces, and the like of an operator from the sensor management unit 3).
Mori disclose operation time predicting circuitry to identify, when the operator detected by the detecting circuitry has no past operation record, an operator with a past operation record who has an attribute most similar to an attribute of the operator with no past operation record based on person attribute information indicating an attribute of each operator(Abstract, a skill-level calculation unit configured to calculate the skill level of the target operator with respect to the work step, the skill level indicating a degree on a spectrum of whether the target operator can suitably accomplish the work step; a second acquisition unit configured to acquire movement data of a model operator at a skill level slightly higher than or equal to the skill level calculated for the target operator; and an instruction determination unit configured to compare the acquired movement data for the model operator with the acquired movement data of the target operator and determine an instruction that allows the movement of the target operator to approach the movement of the model operator on the basis of the results of comparison),
Nakano discloses generate the operation time information indicating the element operation time of the operator with no past operation record (page 4, par.10, Replan worker and machine work assignments and update work plans to optimize. In predicting changes in production capacity, work capacity is predicted in chronological order using a trained model created by a learning method such as machine learning using manufacturing record information 127 and product specification information 126. In updating the work allocation plan, factory production capacity prediction process, the production capacity can be predicted by using the capacity of the worker).
Regarding claim 3, Mori discloses the element operation time indicated by the operation time information is a probability distribution of each element operation time of each operator ([0093], [0094], the feature amounts may be expressed by a probability distribution over time series. The control unit plots calculated feature amounts in time series. This series of processing allows the control unit 11 feature data. FIG. 7 shows an example of converting the raw data acquired from the load cell to convert the raw data acquired from each sensor to time series 31 (peak value). estimates time segments along the time axis during which the target operator operates by analyzing the time series feature data).
Regarding claim 4, Omri discloses the operation time predicting circuitry acquires the probability distribution of each element operation time of an operator with no past operation record by dividing the probability distribution of each element operation time ([0093]-[0094], amounts may be expressed by a probability distribution over time series. The control unit plots calculated feature amounts in time series. This series of processing allows the control unit 11 feature data. The control unit estimates time segments along the time axis during which the target operator operation by analyzing the time series feature data); at a current time, of an operator with a past operation record who has an attribute most similar to an attribute of the operator with no past operation record by a skill rate of the operator with the past operation record who has an attribute most similar to an attribute of the operator with no past operation record (Abstract, [0012], [0017], a skill-level calculation unit configured to calculate the skill level of the target operator with respect to the work step, the skill level indicating a degree on a spectrum of whether the target operator can suitably accomplish the work step; a second acquisition unit configured to acquire movement data of a model operator at a skill level slightly higher than or equal to the skill level calculated for the target operator; and an instruction determination unit configured to compare the acquired movement data for the model operator with the acquired movement data of the target operator and determine an instruction that allows the movement of the target operator to approach the movement of the model operator on the basis of the results of comparison).
Regarding claim 5 and 10-11, combination of Omir and Ooba disclose:
Omir discloses when the operator with no past operation record performs operations a predetermined number of times ([0108], The skill level field stores the skill level of the target worker with respect to the work step at point of time when acquiring the movement data. The learning period field stores number of times (or period) whereby 40 the target worker performed the work step (each process) until the target worker achieves proficiency in performing the work step 40 40 (each process) suitably),
the operation time predicting circuitry determines the operator with no past operation record to be an operator with a past operation record ([0108]-[0110], the learning period filed stores number of times whereby the target worker performed the first process until the target worker achieves proficiency in performing the first process suitably. The reference number field stores the number of times the movement data was referenced as the movement data of a model operator),
generates the operation information based on the operator ([0105], [0108], the skill level field stores the skill level of the target worker) and the type of the product handled by the operator detected by the detecting circuitry ([0108], The process field stores the type of process during which the movement data is acquired); and
Ooba discloses the element operation time measured by the operation time measuring circuitry ([0020], The sensor preferably has the function of measuring an operation time from the start to the end of the sequential operation).
Regarding claims 6 and 12-15, Ooba discloses onsite-operator information generating circuitry (Fig. 1, the camera sensor 1a and 1b) to identify the operator (Fig. 1, operator A and B) being onsite based on the image information and generate the onsite-operator information (Abstract, Fig. 1, an operator monitor unit for monitoring at least one of the motion amount and condition amount of the operation of the operator from the camera 1a and 1b).
Citation Pertinent prior art
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mizuochi (US 20200226849 A1) discloses a worker management system selects a position indicated by the terminal GNSS positional information, as a current position of the worker; a display device for displaying a camera image; a worker detection system mounted on a work machine. The surrounding monitoring sensor is configured to include a sensor capable of detecting a worker near the work machine and at the worksite such as an infrared sensor, a stereo camera.
Trivelpiece et al. (US 20200394589 A1) discloses monitoring workers in a retail environment. The system may include a plurality of cameras located in different regions of the retail location and configured to capture a video feed of a respective region.
Suzuki US20200327482A1) discloses a personnel assignment support system includes a work time period acquiring section that acquires a plurality of work time periods, an evaluating section that stepwisely evaluates a working ability for each of the workers, based on the work time periods acquired by the work time period acquiring section, an ability database that stores a result evaluated by the evaluating section, as a personal ability data for each of the workers, an assignment processing section that executes an assignment program which is executable while referring to the personal ability data.
Subramanian (US20210079627A1) discloses image data may include information relating to an image corresponding to a state of an operation associated with a worksite and a geospatial reference associated with the image.
Law et al. (US20220138963A1) discloses monitoring system is configured to obtain a plurality of images of an operator, determine a motion type of the operator in the plurality of images based on an image recognition model, determine a time of occurrence and a motion period of the motion type, and record the time of occurrence and the motion period of the motion type.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kidest Worku, whose telephone number is 571-272-3737. The examiner can normally be reached on Mon-Fri 9am to 5pm, ET.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119
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