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 .
This communication is responsive to application filed on 04/21/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/08/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-7 are directed to a method.
Claims 8-14 are directed to a system.
Claims 15-20 are directed to a product.
Therefore, claims 1-20 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
An apparatus for automated generation of a manufacturing estimate of a computer model, wherein the apparatus comprises:
at least a processor [e.g. a generic computer element for performing a generic computer function]; and
a memory communicatively connected to the at least a processor [e.g. a generic computer element for performing a generic computer function], the memory containing instructions configuring the at least a processor to:
receive a computer model comprising a plurality of model-based definitions, wherein the computer model is representative of a part to be manufactured [insignificant extra solution, e.g. mere data-gathering and/or insignificant extra-solution activity of transmitting/outputting];
determine a manufacturability of the part to be manufactured as function of the plurality of model-based definitions and a plurality of manufacturing specifications [mathematical concepts and/or [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)]; and
generate a manufacturing estimate as a function of the manufacturability of the part to be manufactured, wherein the manufacturing estimate is generated using a manufacturing machine learning model [insignificant post solution, data output].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional element of “receiving”, “processor”, “memory” and “generating”. The additional element of “receiving” is insignificant pre-solution (i.e. data gathering). The additional elements of “processor” and “memory” recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). In addition, the additional element of “generating” is insignificant post solution, data output. Accordingly, the additional element(s) of each of this claim does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “receiving” is insignificant pre-solutions (i.e. data gathering). At most the additional element is not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “processor” and “memory” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In addition, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “generating” in insignificant post-solutions (i.e. mere data output). At most the additional element is not found to including anything more than mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iii)- presenting offers to potential customers.
As per claim 2, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 3, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 4, the claim falls into [mathematical concepts].
As per claim 5, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 6, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 7, the claim falls into [e.g. a generic computer element for performing a generic computer function].
As per claim 8, the claim falls into [insignificant post solution, data output].
As per claim 9, the claim falls into [e.g. a generic computer element for performing a generic computer function].
As per claim 10, the claim falls into [e.g. a generic computer element for performing a generic computer function].
As per Claims 11-20, claims 11-20 recite limitations analogous in scope to those of claims 1-10, and as such are similar rejected.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2022/0390918 issued to Atev et al in view of US Publication No. 2015/0127480 A1 issued to Herrman et al.
1. Atev et al discloses an apparatus for automated generation of a manufacturing estimate of a computer model, wherein the apparatus comprises:
at least a processor (See: par [0054] Computer system 500 includes a processor 504 and a memory 508 that communicate with each other, and with other components, via a bus 512); and
a memory communicatively connected to the at least a processor (See: par [0054] Computer system 500 includes a processor 504 and a memory 508 that communicate with each other, and with other components, via a bus 512), the memory containing instructions configuring the at least a processor to:
receive a computer model comprising a plurality of model-based definitions, wherein the computer model is representative of a part to be manufactured (See: par [0018] Computing device 104 may be configured to receive a computer model 108. As used in this disclosure, a “computer model” is a virtual representation….The computer model further includes information about the geometry and/or other defining properties of the mechanical part's structure. computer model 108, in some cases, may include a triangulated surface (e.g., .STLfile). In some cases, computer model 108 may be representative of a part for manufacture);
determine a manufacturability of the part to be manufactured as function of the plurality of model-based definitions and a plurality of manufacturing specifications (See: par [0018] The computer model further includes information about the geometry and/or other defining properties of the mechanical part's structure. computer model 108, in some cases, may include a triangulated surface (e.g., .STLfile). In some cases, computer model 108 may be representative of a part for manufacture. As used in this disclosure, a “part” is any physical object that is manufactured. A part may be designed for manufacture, for example by using computer-aided design software; par [0025] manufacturing request datum may include at least an element of part data. Part data may include any descriptive attributes of manufacturing request datum. “Descriptive attributes,” as used in this disclosure, are any features, limitations, details, restrictions and/or specifications of manufacturing request datum. Descriptive attributes may include, without limitation, any features, limitations, details, restrictions and/or specifications relating to the CNC mechanical part geometry, materials, finishes, connections, hardware, special processes, dimensions, tolerances, and the like. Descriptive attributes may further include, without limitation, any features, limitations, details, restrictions, and/or specifications relating to a total request for manufacture, such as total amount of CNC mechanical parts, restrictions on deadline to have request completed, and the like. As an example and without limitation, part data may include part count data, i.e., quantity, that contains the total number of each part included in manufacturing request datum, such as without limitation a request to have a total number of 24 brackets manufactured. As a further example and without limitation, part data may include part face count data that contains a total number of faces on part included in manufacturing request datum, such as without limitation a price request to have a hollow box with a total of 10 faces manufactured. As another example and without limitation, part data may include part material data that contains material of part, such as without limitation a quote request for a steel roller bushing).
Atev et al does not specify but Herrman et al discloses generate a manufacturing estimate as a function of the manufacturability of the part to be manufactured (see: [0014] Furthermore, the method S100 can automatically detect a possible or preferred fixturing method for the real part--such as for a real part that is machined (e.g., turned, milled)--in response to insertion of a first (or otherwise early) virtual geometry into the CAD program, automatically estimate a cost to manufacture a custom fixture (if relevant, such as based on a quantity of units of the part specified for the order), and calculate a per-unit fixturing for manufacture of the real part according to the fixturing method to calculate a total cost to manufacture units of a part in a current order….The method S100 can thus automatically predict a fixturing method that lowers a manufacturing cost to the user for the order, increases probability of meeting requirements (e.g., tolerances) of the part, and/or diminishes lead time for delivery of the order (e.g., based on machining center availability, calculated machine time), etc., and the method S100 can automatically generate a virtual model of a fixture for the real part and transmit this virtual model (and/or code in machining center language for manufacturing the fixture, etc.) to the manufacturing facility with submission of the order to the manufacturing facility. Alternatively, the method S100 can automatically generate various fixturing-related details--such as fixture type, machining operation schedule, and projected fixture cost--locally at the workstation and then submit these details to a server affiliated with the manufacturing facility; an application executing on the server can then generate a virtual model of the fixture for the real part based on these details), wherein the manufacturing estimate is generated using a manufacturing machine learning model (See: par [0024] A quote file can be generated automatically by the manufacturing facility. In one example, a model of production jobs at the manufacturing facility, including stock, tooling, machining center, cost, and other data, is created over time as subsequent jobs are completed; the component and total costs for these jobs are then analyzed to generate one or more pricing models (e.g., pricing algorithms) once a sufficient volume of data is collected, such as based on a target machine utilization time, target revenue or profit, target job lead time, target manufacturing capacity, etc.; and the pricing model(s) is updated over time as additional job data is collected (e.g., according to machine learning techniques) and/or as target machine utilization time, target revenue or profit, target job lead time, target manufacturing capacity, etc. are adjusted; par [0078] The method S100 can further implement supervised or semi-supervised machine learning to improve the manufacturing and quote model over time as additional representative changes are submitted for various part orders, such as by automatically updating a pricing model defined in the quote file or by modifying a fixturing engine for generating virtual models of custom fixtures).
It would have been obvious before the effective filing date to combine quoting manufacture of a real part during construction of a virtual model as taught by Herrman et al to manufacture orientation using machine learning of Atev et al would be to improve ease of manufacture and reduce part cost, or to suggest an alternative material for the real part (Herrman et al, par [0076]).
2. Herrman et al discloses the apparatus of claim 1, wherein the memory contains additional instructions configuring the processor to determine the manufacturability of the part to be manufactured as function of a cost and a time (See: par [0017] to estimate a cost to manufacture one or more units of the real part. Alternatively, the quote file can specify a variable per-unit-time cost, such as in the form of a continuous or stepped time-cost function that specifies variances in (average) cost per unit of time of continuous machining center use over time. The variable per-unit-time cost can reflect changing electricity costs during a work day, variable shop pricing per hour (e.g., for overtime, for fully-automated work periods and semi-automated work periods within the manufacturing facility), machining center needs for other confirmed jobs in a calendar or schedule for the manufacturing facility).
4. Atev et al discloses the apparatus of claim 1, wherein the plurality of model-based definitions comprises at least geometric dimensioning and tolerancing information (See: par [0024] computing device 104 may be additionally configured to receive an element of part data. As used in this disclosure, “part data” is information related to a part. Non-limiting examples of part data include quantity, material, requested lead time, surface finish, manufacturing process, and dimensional tolerance. In some cases, computing device 104 may select machine learning model 112 as a function of an element of part data or computer model 108. For example, in some cases, element of data may be used to determine an appropriate manufacturing process and a machine learning model will be selected based upon that manufacturing process; par [0025] manufacturing request datum may include at least an element of part data. Part data may include any descriptive attributes of manufacturing request datum. “Descriptive attributes,” as used in this disclosure, are any features, limitations, details, restrictions and/or specifications of manufacturing request datum. Descriptive attributes may include, without limitation, any features, limitations, details, restrictions and/or specifications relating to the CNC mechanical part geometry, materials, finishes, connections, hardware, special processes, dimensions, tolerances, and the like).
5. Herrman et al discloses the apparatus of claim 1, wherein the plurality of model-based definitions comprises at least an assembly level bill of materials (See: par [0042] automatically generate a part file for the user-selected motor, automatically download a part file for the motor from a manufacturer or supplier database, and/or generate a bill of materials, such as including the motor and bolts, screws, washers, nuts, and other mounting hardware to assembled the motor on the modeled part. However, Block S130 can function in any other way to receive, select, or identify a suitable material for the part in the part file).
6. Atev et al discloses the apparatus of claim 1, wherein the plurality of model-based definitions comprises at least component materials (See: par [0024] computing device 104 may be additionally configured to receive an element of part data. As used in this disclosure, “part data” is information related to a part. Non-limiting examples of part data include quantity, material, requested lead time, surface finish, manufacturing process, and dimensional tolerance. In some cases, computing device 104 may select machine learning model 112 as a function of an element of part data or computer model 108. For example, in some cases, element of data may be used to determine an appropriate manufacturing process and a machine learning model will be selected based upon that manufacturing process; par [0025] manufacturing request datum may include at least an element of part data. Part data may include any descriptive attributes of manufacturing request datum. “Descriptive attributes,” as used in this disclosure, are any features, limitations, details, restrictions and/or specifications of manufacturing request datum. Descriptive attributes may include, without limitation, any features, limitations, details, restrictions and/or specifications relating to the CNC mechanical part geometry, materials, finishes, connections, hardware, special processes, dimensions, tolerances, and the like).
7. Herrman et al discloses the apparatus of claim 1, wherein the plurality of manufacturing specifications comprises at least a run time (See: [0015] The method S100 can be implemented within a CAD modeling program (or software, engine, etc.) executing on a computing device, such as a desktop computer, laptop computer, or tablet. For example, the method S100 can execute within a real-time quoting plug-in and/or a design-for-manufacturability (DFM) plug-in executing within the CAD program to retrieve a quote file over the Internet and to compare virtual model features with the quote file locally to generate and display the manufacturing quote in real-time within the CAD program).
8. Atev et al discloses the apparatus of claim 1, wherein the memory further contains instructions configuring the processor to generate a manufacturability score as a function of manufacturability of the part to be manufactured (See: par [0020] a machine learning model 112 may be used to score each candidate orientation before a pairwise ranking function is used, and the score for each candidate orientation is used in the pairwise ranking function; alternatively or additionally, in some cases, necessary scoring for comparing pairs of candidate orientations is performed by pairwise ranking function; par [0042] Boolean Model may be a simple baseline model following underlying principles of relational algebra with algebraic expressions and where orientations are not generated unless they completely match, for example according to a manufacturing metric. A Vector Space Model may include vectors representative of computer model and/or part data features. Vectors may be assigned with weights. Weights may be ranged from positive (if matched completely to an orientation based upon some metric) to negative (if unmatched or completely oppositely matched). A similarity score between a computer model and/or an element of part data and an orientation can be found by calculating a cosine value between an input weight vector and an output weight vector using cosine similarity. Orientation can be generated and ranked according to similarity score and generated top k orientations which have highest scores or are most relevant to an input (computer model and/or part data) vector).
9. Atev et al discloses the apparatus of claim 1, wherein the memory further contains instructions configuring the processor to encode the plurality of model-based definitions into the computer model (See: par [0018] The computer model further includes information about the geometry and/or other defining properties of the mechanical part's structure).
10. Herrman et al discloses the apparatus of claim 1, wherein the plurality of manufacturing specifications are generated using a domain specific language (See: par [0014] he method S100 can automatically generate a virtual model of a fixture for the real part and transmit this virtual model (and/or code in machining center language for manufacturing the fixture, etc.) to the manufacturing facility with submission of the order to the manufacturing facility).
As per Claims 11, 12, and 14-20, claims 11, 12, and 14-20 recite limitations analogous in scope to those of claims 1, 2, and 4-10, and as such are similar rejected.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Atev et al and Herrman et al as applied to claims 1, and 11 above, and further in view of US Publication No. 2006/0052892 A1 issued to Matsushima et al.
3. Neither Atev et al nor Herrman et al discloses but Matsushima et al discloses determine unmanufacturable qualities (See: par [0038] Further, when the metal mold is created for manufacturing the product sample, the possible problem in the actual manufacturing process can be verified, and thereby design errors such as designing an unmanufacturable product can be checked in advance and prevented).
It would have been obvious before the effective filing date to combine 3D CAD-based model as taught by Matsushima et al to manufacture orientation using machine learning of Atev et al would be to reduce the required time to determine a product design (Matsushima et al, par [0009]).
As per Claim 13, claim 13 recites limitations analogous in scope to those of claim 3, and as such are similar rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marusich et al (US Patent No. 11, 567, 484 B1) discloses analyzing machinability of a part for manufacture, wherein the apparatus comprises a processor, receive a representative part model of a part for manufacture, extract a semantic datum from the print of the part for manufacture, a manufacturing quote is generated as a function of the machinability datum (Abstract).
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 07/17/2026
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