DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 04/09/2026
Claims 1, 3-6, 11, 13-15, 19, 21-23, and 25-31 are presented for examination
Finality
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Response to Arguments - 101
Applicant's arguments filed 04/09/2026 have been fully considered but they are not persuasive.
Applicant argues that the newly made amendments successfully integrate the claims into a practical application/ provide significantly more.
Examiner responds by explaining that the additional elements are not sufficient to integrate the claims into a practical application nor provide significantly more. Particularly, the newly added neural network limitations are highly generic and amount to no more than mere instructions to apply a mental process.
For the new limitation of “using a recurrent neural network (RNN) model of the neural network model, generate an estimated set of alphanumeric fastener parameter values based on the plurality of partial parameter values, and add the estimated set of alphanumeric fastener parameter values to the initial set of alphanumeric fastener parameter values to generate a revised set of alphanumeric fastener parameter values;” creating this revised set of alphanumeric fastener parameter values is a mental process equivalent to observing a set of partial parameter values and extrapolating additional values based on the already present values. For example, if the partial values define {slot-type: Phillips} and {length: 10cm} a person could reasonably judge that the faster being described is a screw and add an additional parameter value {fastner-type:screw}. Creating a revised parameter set including this new value is merely the act of writing all the values, both initial and new, in a single set as with a pencil and paper.
Performing this process “using a recurrent neural network (RNN) model of the neural network model,” without any specificity as to how the neural network is used or actually applied to the problem at hand amounts to no more than mere instructions to apply.
Further, finding fasteners that match the given set of parameters is merely a mental process equivalent to a person, such as a construction worker or carpenter, searching a hardware store for a certain type of fastener. The person may decide that, based on the needs of an ongoing project, they need Phillips head wood screws. Most hardware stores keep fasteners in organizers that group fasteners by type, such as intended use, head type, etc. that have visual keys to show where different types of hardware are stored. With this in mind such a worker would consult the key of the organizer, based on the desired fastener features, to find a collection of fasteners that meet their needs, such as a group of Phillips head wood screws of varying lengths. This kind of searching could also be done by observing a series of images of different fasteners, comparing the features of each, and judging if one or more meet the desired criteria. Determining that a matching fastener does not exist merely amounts to performing this search, having checked every option, and coming up empty handed. In other words, comparing each available fastener to the desired properties and judging, after exhausting all available fasteners, that none match these requirements.
See the July 2024 Patent Subject Matter Eligibility update: "A claim to “the collection of information from various sources (a Federal database, a State database, and a case worker) and understanding the meaning of that information (determining whether a person is receiving SSDI benefits and determining whether they are eligible for benefits under the law),” where “ `[t]hese steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). "
Also see in the July 2024 Patent Subject Matter Eligibility update: "Claims to “the use of an algorithm-generated content-based identifier to perform the claimed data-management functions,” which include limitations to “controlling access to data items,” “retrieving and delivering copies of data items,” and “marking copies of data items for deletion,” where the claims cover “a medley of mental processes that, taken together, amount only to a multistep mental process,” such that the steps can be practically performed in the human mind, PersonalWeb Techs. LLC v. Google LLC, 8 F.4th 1310, 1316-18 (Fed. Cir. 2021)."
As such, searching for and retrieving matching fasteners from a database or repository is a mental process.
If a matching fastener is not found, creating a new fastener with the desired features by combining existing fastener designs is also a mental process. Choosing a pair of fasteners from a known set is a mental process equivalent to judging which fasteners to focus on. For example, if the parameter set defines {slot-type: Phillips} and {length: 10cm}, this could involve selecting a 20cm Phillips-head screw and a 10cm hex-head screw. A merged design combining the features of both fasteners can be created by observing both, choosing a set of features from each, and creating, with a pencil and paper, a depiction of a new fastener with those properties. For example, the head of the Phillips-head screw could be combined with the 10cm screw length of the hex-head screw to create a new fastener with the desired {slot-type: Phillips} and {length: 10cm} properties. This depiction could be a written list of parameters for this new fastener.
A person could also combine two fasteners purely visually by observing images of those fasteners and drawing, with a pencil and paper, a new fastener that combines the features of both images. For example, a person could observe an image of the 20mm Phillips head bolt and an image of the 5mm hex head bolt and draw a new bolt that is roughly 20mm long and has a hex head.
Finally, comparing the property values to the representation generated visually is a mental process equivalent to observing both drawing and judging how similar they are and if the features are consistent (i.e. are they both hex heads, are they both roughly 20mm, etc.)
Further, as to the applicant’s arguments that the claimed invention “reduces resource usage,” i.e. makes it so that a particular fastener design can be used for different purposes and therefore fewer specialized fasteners are required, is not reflected in the claims and therefore is moot. Further, even if the requirement that the combined fastener was multifunctional was reflected in the claim, this is merely a result of the mental process of designing the combined fastener, and therefore cannot be the basis for integration into a practical application.
As for the newly added claims (29-31,) the content of these claims is also not sufficient to integrate the claims into a practical application.
wherein executing the RNN model further comprises: generating a feature vector from the first set of features and the second set of features, the feature vector transforming the first set of features and the second set of features into a vector space; and executing the RNN model on the feature vector.
Generating a feature vector that transforms the features into a vector space is merely the act of mentally encoding those features into a vector format. For example, one-hot encoding is a form of encoding used in machine learning in which each possible feature value has a corresponding unique vector with zeros and a single one. For example, a Phillips slot might correspond to the vector [0,1,0] while a hex head slot might correspond to the vector [1,0,0]. Translating these features to vector space would merely be the act of determining the corresponding vector for the particular value, i.e. if the given feature is a Phillips slot, giving the corresponding vector [0,1,0].
“Executing” the RNN model on this feature vector, without any specificity as to how this execution is actually performed amounts to no more than mere instructions to apply.
Response to Arguments - 103
Applicant’s arguments, see Pages 16-18, filed 04/09/2026, with respect to the rejection of claims 1, 3-6, 11, 13-15, 19, 21-23, and 25-28 under 103 have been fully considered and are persuasive. The rejection of claims 1, 3-6, 11, 13-15, 19, 21-23, and 25-28 under 103 has been withdrawn.
Particularly, no prior art reference that discloses specifically merging design features of fasteners using a combination of an RNN and CNN with explicitly alphanumeric parameter values, (i.e. excluding visual parameters) nor a combination of references that one of ordinary skill in the art would have been reasonably motivated to combine to teach these features, was found.
Claim Objections
Claims 3, 13, 25, and 29-31 objected to because of the following informalities:
Claims 3, 13, and 25 recite “extracting a second set of features from a context of the fastener, the context extracted from a design tool that designs an environment of the fastener;” It is not immediately clear which fastener is referred to here, as a fastener, first fastener, second fastener, and merged fastener were previously introduced. To avoid potential issues with antecedent basis, it is recommended to amend these claims to more clearly specify which fastener is being referred to.
Appropriate correction is required.
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, 3-6, 11, 13-15, 19, 21-23, and 25-31 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
A method comprising: obtaining an initial set of alphanumeric fastener parameter values for a fastener, the initial set of alphanumeric fastener parameter values comprising a plurality of partial parameter values;
Obtaining the initial set of fastener parameter values is a mental process equivalent to judging what type of fastener would be best suited for a particular application; for example, a person with who needs to attach two 5cm-thick pieces of wood to each other who only has a Phillips head driver might judge that a fastener that accepts a Phillips head slot and is at least 10cm thick would be required. These parameter values could further be written down using a pencil and paper.
executing a neural network model to: using a recurrent neural network (RNN) model of the neural network model, generate an estimated set of alphanumeric fastener parameter values based on the plurality of partial parameter values, and add the estimated set of alphanumeric fastener parameter values to the initial set of alphanumeric fastener parameter values to generate a revised set of alphanumeric fastener parameter values; and
Creating this revised set of alphanumeric fastener parameter values is a mental process equivalent to observing a set of partial parameter values and extrapolating additional values based on the already present values. For example, if the partial values define {slot-type: Phillips} and {length: 10cm} a person could reasonably judge that the faster being described is a screw and add an additional parameter value {fastner-type:screw}. Creating a revised parameter set including this new value is merely the act of writing all the values, both initial and new, in a single set as with a pencil and paper.
Performing this process “using a recurrent neural network (RNN) model of the neural network model,” without any specificity as to how the neural network is used or actually applied to the problem at hand amounts to no more than mere instructions to apply.
using features extracted at least from the revised set of alphanumeric fastener parameter values to query a fastener description repository; obtaining, from the fastener description repository, a set of possible matching fasteners; determining that a matching fastener does not exist;
Finding fasteners that match the given set of parameters is merely a mental process equivalent to a person, such as a construction worker or carpenter, searching a hardware store for a certain type of fastener. The person may decide that, based on the needs of an ongoing project, they need Phillips head wood screws. Most hardware stores keep fasteners in organizers that group fasteners by type, such as intended use, head type, etc. that have visual keys to show where different types of hardware are stored. With this in mind such a worker would consult the key of the organizer, based on the desired fastener features, to find a collection of fasteners that meet their needs, such as a group of Phillips head wood screws of varying lengths. This kind of searching could also be done by observing a series of images of different fasteners, comparing the features of each, and judging if one or more meet the desired criteria. Determining that a matching fastener does not exist merely amounts to performing this search, having checked every option, and coming up empty handed. In other words, comparing each available fastener to the desired properties and judging, after exhausting all available fasteners, that none match these requirements.
See the July 2024 Patent Subject Matter Eligibility update: "A claim to “the collection of information from various sources (a Federal database, a State database, and a case worker) and understanding the meaning of that information (determining whether a person is receiving SSDI benefits and determining whether they are eligible for benefits under the law),” where “ `[t]hese steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). "
Also see in the July 2024 Patent Subject Matter Eligibility update: "Claims to “the use of an algorithm-generated content-based identifier to perform the claimed data-management functions,” which include limitations to “controlling access to data items,” “retrieving and delivering copies of data items,” and “marking copies of data items for deletion,” where the claims cover “a medley of mental processes that, taken together, amount only to a multistep mental process,” such that the steps can be practically performed in the human mind, PersonalWeb Techs. LLC v. Google LLC, 8 F.4th 1310, 1316-18 (Fed. Cir. 2021)."
Additionally, should it be found that “query a fastener description repository” is not a mental process, it is also considered mere instructions to apply and well-understood, routine, conventional activity, as analyzed below.
selecting a first fastener and a second fastener; generating, using the neural network model, a merged fastener design based on the first fastener and the second fastener, wherein generating the merged fastener design comprises: executing the RNN model to merge a first set of alphanumeric parameter values from the first fastener and a second set of alphanumeric parameter values from the second fastener to generate a set of estimated property values of the merged fastener design;
Choosing a pair of fasteners from a known set is a mental process equivalent to judging which fasteners to focus on. For example, if the parameter set defines {slot-type: Phillips} and {length: 10cm}, this could involve selecting a 20cm Phillips-head screw and a 10cm hex-head screw. A merged design combining the features of both fasteners can be created by observing both, choosing a set of features from each, and creating, with a pencil and paper, a depiction of a new fastener with those properties. For example, the head of the Phillips-head screw could be combined with the 10cm screw length of the hex-head screw to create a new fastener with the desired {slot-type: Phillips} and {length: 10cm} properties. This depiction could be a written list of parameters for this new fastener.
Specifying that these operations are performed by an RNN is simply the act of instructing a computer to perform generic neural networks operations to carry out the mental process of combining the features of the fasteners, and therefore amounts to no more than mere instructions to apply a judicial exception using a generic computer.
executing a convolutional neural network (CNN) model of the neural network model using a first image of the first fastener and a second image of the second fastener to generate an image of the merged fastener design;
A person could combine two fasteners purely visually by observing images of those fasteners and drawing, with a pencil and paper, a new fastener that combines the features of both images. For example, a person could observe an image of the 20mm Phillips head bolt and an image of the 5mm hex head bolt and draw a new bolt that is roughly 20mm long and has a hex head.
Specifying that these operations are performed by a CNN is simply the act of instructing a computer to perform generic neural networks operations to carry out the mental process of combining the features of the fasteners, and therefore amounts to no more than mere instructions to apply a judicial exception using a generic computer.
and comparing the set of estimated property values of the merged fastener design with the image of the merged fastener design to detect convergence; and
Comparing the property values to the representation generated visually is a mental process equivalent to observing both drawing and judging how similar they are and if the features are consistent (i.e. are they both hex heads, are they both roughly 20mm, etc.)
Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and post solution activity to be insignificant extra-solution activity.
Post-Solution Activity:
presenting the merged fastener design
This element merely acts on and presents the results of the previous abstract steps. A claim element that merely acts on and presents the results of a series of previous abstract steps is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
executing a neural network model to: using a recurrent neural network (RNN) model of the neural network model, generate an estimated set of alphanumeric fastener parameter values … executing the RNN model … executing a convolutional neural network (CNN) model of the neural network model
Specifying that these operations are performed by a neural network at a high level of generality is simply the act of instructing a computer to perform generic neural network functions to carry out the mental process of searching for the desired fasteners and coming up with combinations of those fasteners, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the neural network is “executed,” estimated parameter values are “generated,” the repository is “queried,” and the merged design is created without reciting how this execution, querying, or merging is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations.
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Additionally, querying a repository (i.e. a database) and retrieving data from it in a generic manner is also an example of using a computer merely as a tool to execute/apply the abstract idea. See MPEP § 2106.05(f)(2): “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit) … TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.”
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere data gathering or post solution activity (Insignificant Extra-Solution Activity), Well-Understood, Routine, Conventional Activity, or a general purpose computer and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and post solution activity to be insignificant extra-solution activity.
Post-Solution Activity:
presenting the merged fastener design
This element merely acts on and presents the results of the previous abstract steps. A claim element that merely acts on and presents the results of a series of previous abstract steps is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Insignificant application) i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) and ii. Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.)
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
executing a neural network model to: using a recurrent neural network (RNN) model of the neural network model, generate an estimated set of alphanumeric fastener parameter values … executing the RNN model … executing a convolutional neural network (CNN) model of the neural network model
Specifying that these operations are performed by a neural network at a high level of generality is simply the act of instructing a computer to perform generic neural network functions to carry out the mental process of searching for the desired fasteners and coming up with combinations of those fasteners, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the neural network is “executed,” estimated parameter values are “generated,” the repository is “queried,” and the merged design is created without reciting how this execution, querying, or merging is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations.
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Additionally, querying a repository (i.e. a database) and retrieving data from it in a generic manner is also an example of using a computer merely as a tool to execute/apply the abstract idea. See MPEP § 2106.05(f)(2): “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit) … TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.”
In addition, the following are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d):
“query a fastener description repository”
Querying and retrieving data from a database is equivalent to storing and retrieving information in memory as well as generic electronic recordkeeping (MPEP § 2106.05(d)(II) iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
“executing a convolutional neural network (CNN) model of the neural network model using a first image … and a second image … to generate an image of the merged … design;”
A Review of Multimodal Medical Image Fusion Techniques – ([Page 2 Par 1])
Deep learning for pixel-level image fusion: Recent advances and future prospects ([Page 161 Col 2 Par 7])
US 20190287215 A1 ([Abstract]
US 20190096046 A1 ([Abstract])
US 10467503 B1 ([Col 6 line 57-64])
“executing the RNN model to merge a first set of alphanumeric parameter values … and a second set of alphanumeric parameter values … to generate a set of estimated property values”
US 20180206797 A1 ([Par 29, 40])
US 20180089888 A1 ([Par 50])
US 20180053108 A1 ([Par 38, 89, 108])
US 20170200065 A1 ([Par 76])
As per MPEP § 2106.05(d), an additional element that is “no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality,” does not integrate a judicial exception into a practical application, nor provide significantly more.
Moreover, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “executing a neural network model to: using a recurrent neural network (RNN) model of the neural network model, generate an estimated set of alphanumeric fastener parameter values, a fastener description repository, executing the RNN model, executing a convolutional neural network (CNN) model of the neural network model” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 11 The elements of claim 1 are substantially the same as those of claim 1. Therefore, the elements of claim 11 are rejected due to the same reasons as outlined above for claim 1.
Further, as to the elements present in claim 11 but absent in claim 1:
Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “A system comprising: a fastener description repository comprising a plurality of fastener descriptions; and a computer processor configured to perform operations, the operations comprising: … executing a neural network model to: using a recurrent neural network (RNN) model of the neural network model, generate an estimated set of alphanumeric fastener parameter values … executing the RNN model … executing a convolutional neural network (CNN) model of the neural network model” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
wherein the set of estimated property values comprises one or more of a geometric property, a mechanical property, or a material composition property;
This merely clarifies the form of the property values, and is therefore merely an extension of the mental process and mere instructions to apply of determining those merged property values.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 19 The elements of claim 19 are substantially the same as those of claim 11. Therefore, the elements of claim 19 are rejected due to the same reasons as outlined above for claim 11.
Claim 3 recites “The method of claim 1, wherein executing the RNN model comprises: extracting a first set of features from the initial set of alphanumeric fastener parameter values; and extracting a second set of features from a context of the fastener, the context extracted from a design tool that designs an environment of the fastener; and executing the RNN model on the first set of features and the second set of features.”
Extracting features from parameters or a design context is a mental process equivalent to observing a set of parameters or design context, for example coordinate information, and making a judgement about the features defined by those parameters. For example, a person could look at the coordinate set ((0,0), (1,0), (1,1), (0,1)) and determine that the shape defined by those coordinates is a square.
Specifying that these operations are performed by an RNN is simply the act of instructing a computer to perform generic neural network operations to carry out the mental process and therefore amounts to no more than mere instructions to apply a judicial exception using a generic computer.
See (MPEP 2106.05(f)(2)(i)) “A commonplace business method or mathematical algorithm being applied on a general purpose computer,” [Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ]
Further, should it be found that this extraction is not a mental process, it is also an example of mere data gathering.
“Extracting” this data in such a generic manner is equivalent to merely gathering data representative of features from the parameter values and context, and therefore amounts to no more than mere data gathering.
A claim element that amounts to merely gathering data is not indicative of integration into a
practical solution nor evidence that the claim provides an inventive concept or significantly more, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Claim 4 recites “The method of claim 1, further comprising: obtaining a submitted image of the fastener, and wherein executing the neural network model to query the fastener description repository comprises executing the CNN model to classify the submitted image based on a plurality of stored images in the fastener description repository.”
“Obtaining” such an image, when recited at such a high level of generality without any specifics as to how the obtaining is actually performed, amounts to no more than mere data gathering.
Classifying an image is a mental process that is possible to perform in the human mind. For example, a person could observe an image of a cat and judge that the animal in the image is a cat.
Claim 5 recites “The method of claim 4, wherein executing the RNN model comprises using the revised set of alphanumeric fastener parameter values for obtaining a first set of possible matching fasteners, wherein executing the CNN model comprises obtaining a second set of possible matching fasteners based on classifying the submitted image, and wherein the method further comprises comparing the first set of possible matching fasteners to the second set of possible matching fasteners to determine whether the matching fastener exists.”
Obtaining a set of possible matching fasteners given a list of fastener parameters and a set of fasteners, such as in a database or repository, is a mental process that is merely the act of matching the given parameters to those of the fasteners in the set by observing the parameters of each fastener and judging whether or not they match the given parameters.
Using the RNN to perform this mental process, when recited at such a high level of generality without any specificity as to how the RNN is actually utilized, amounts to no more than mere instructions to apply.
Obtaining a set of possible matching fasteners given a classified image of a fastener and a set of fasteners, such as in a database or repository, is a mental process that is merely the act of matching the fastener in the image to those of the fasteners in the set by observing the image and its classified labels to those of each fastener and judging whether or not they match. For example, if the classified image shows a hex-head screw that is 15cm in length, this would consist of searching through the database/repository for a hex-head screw that is 15cm.
Determining if a something exists in at least one of a plurality of sets of objects is a mental process equivalent to observing each object in each set and judging whether or not it matches the object being searched for.
Claim 6 recites “The method of claim 5, wherein comparing the first set of possible matching fasteners to the second set of possible matching fasteners comprises comparing alphanumeric identifiers assigned to the first set of possible matching fasteners and the second set of possible matching fasteners.”
Determining if a something exists in at least one of a plurality of sets of objects based on its label is a mental process equivalent to observing each object and its associated label in each set and judging whether or not the label matches the label for object being searched for.
Claim 21 recites wherein each of the plurality of stored images in the fastener description repository corresponds to an individual class.
Classifying an image is a mental process that is possible to perform in the human mind. For example, a person could observe an image of a cat and judge that the animal in the image is a cat. Specifying that only one class or category is associated with each image merely clarifies the content of the data contained within the repository.
Claim 29 recites “wherein executing the RNN model further comprises: generating a feature vector from the first set of features and the second set of features, the feature vector transforming the first set of features and the second set of features into a vector space; and executing the RNN model on the feature vector.”
Generating a feature vector that “transforms” the features into a vector space is merely the act of mentally encoding those features into a vector format. For example, one-hot encoding is a form of encoding used in machine learning in which each possible feature value has a corresponding unique vector with zeros and a single one. For example, a person might decide that a Phillips slot should correspond to the vector [0,1,0] while a hex head slot should correspond to the vector [1,0,0]. Translating these features to vector space is therefore merely the act of mentally determining the corresponding vector for the particular value, i.e. if the given feature is a Phillips slot, giving the corresponding vector [0,1,0].
“Executing” the RNN model on this feature vector, without any specificity as to how this execution is actually performed amounts to no more than mere instructions to apply.
Claim 13-15, 22-23, and 30 The elements of claims 13-15, 22-23, and 30 are substantially the same as those of claims 3-6, 21, and 29. Therefore, the elements of claims 13-15, 22-23, and 30 are rejected due to the same reasons as outlined above for claims 3-6, 21, and 29.
Claims 25-28 and 31 The elements of claims 25-28 and 31 are substantially the same as those of claims 3-6 and 29. Therefore, the elements of claims 25-28 and 31 are rejected due to the same reasons as outlined above for claims 3-6 and 29.
Conclusion
Prior art of note that is not relied upon is made of record below:
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
Multimodal Convolutional Neural Networks for Matching Image and Sentence
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/M.P.M./ Examiner, Art Unit 2187
/EMERSON C PUENTE/ Supervisory Patent Examiner, Art Unit 2187
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