Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-19, 22 and 28 are rejected under 35 U.S.C. sec. 102(a)(2) as being anticipated by United States Patent Application Pub. No.: US20190389057A1 to Chae that was filed in 2019 and who is assigned to LG™ (hereinafter “CHAE”).
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In regard to claim 1, 22 and 28, Chae discloses “…1. (Original) A method implemented by one or more processors, the method comprising:
capturing, using one or more vision components, vision data instances throughout an environment of at least one robot;
processing the vision data instances to identify regions of interest in the
environment and to determine, for each of the regions of interest: (See paragraph 56-72 where the device can include an optical sensor and a neural network to provide an indication of the object location in the environment and see FIG. 1-5 where the robot can receive a speech command to search for the remote controller and the robot includes a camera to search the area for the object and then provide a notification back to the user about the result of the object search)
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an estimated location of the region of interest, and
a region embedding, for the region of interest, that is in a natural language
embedding space and that semantically corresponds to visual features of the
region of interest; for each of the regions of interest, storing an association of the estimated location
of the region of interest to the region embedding for the region of interest; (See FIG. 12 where the estimated location of the remote control can be the home and the robot can search the home with the sensors and the natural language instruction and then determine the exact location of the remote controls in the home)
identifying an instruction for a robot to perform a task, the instruction being a free-form natural language instruction generated based on user interface input that is provided by a user via one or more user interface input devices;
determining, based on the instruction, object descriptors that each describe a
corresponding candidate environmental object relevant to performance of the task; (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
comparing object descriptor embeddings, for the object descriptors, to the region embeddings, for the regions of interest, to identify:
a subset of the object descriptors that each describe a corresponding object
that is likely present in the environment;
responsive to identifying the subset of object descriptors, processing the subset of object descriptors and the instruction, using a large language model (LLM), to generate LLM output that models a probability distribution, over candidate word compositions, that is dependent on the object descriptors and on the instruction; determining, based on the LLM output
and a skill description that is a natural (see paragraph 164-199)
language description of a robotic skill performable by the robot, to implement the robotic skill; and in response to determining to implement the robotic skill: causing the robot to implement the robotic skill in the environment. (See paragraph 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons)
Chae discloses “…2. (Original) The method of claim 1, wherein the natural language description of the robotic skill includes a skill action descriptor and a skill object descriptor. (See paragraph 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons)
Chae discloses “…3. (Original) The method of claim 2, further comprising:
identifying a given region of interest, of the regions of interest, based on comparing a skill object descriptor embedding, for the skill object descriptor, to the region embedding for
the given region of interest;
in response to identifying the given region of interest, using the estimated location of the region of interest in causing the robot to implement the robotic skill in the environment.” (See paragraph 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons)
Chae discloses “…4. (Original) The method of claim 3, wherein the robotic skill is a navigation skill and wherein using the estimated location of the region of interest in causing the robot to implement the robotic skill in the environment comprises:
causing the robot to navigate to a particular location that is determined based on the estimated location. (See paragraph 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…5. (Original) The method of claim 3, further comprising:
identifying an additional given region of interest, of the regions of interest, based on comparing the skill object descriptor embedding, for the skill object descriptor, to the region embedding for the additional given region of interest;
determining, based on the estimated location of the region of interest and the
estimated location of the additional region of interest, that the region of interest and the additional region of interest correspond to a same object; and
in response to determining that the region of interest and the additional region of interest correspond to the same object, using the estimated location of the region of interest and the estimated location of the additional region of interest in causing the robot to implement the robotic skill in the environment. (See paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…6. (Original) The method of claim 5, wherein the robotic skill is a navigation skill and wherein using the estimated location of the region of interest and the estimated location of the additional region of interest in causing the robot to implement the robotic skill in the environment comprises: determining a particular location as a function of the estimated location of the region of interest the estimated location of the additional region of interest; and causing the robot to navigate to the particular location. (See paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…7. (Original) The method of claim 5, wherein determining that the region of interest and the additional region of interest correspond to the same object is further based on comparing a first size, of the first region of interest, to a second size, of the second region of interest. (See paragraph 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…8. (Original) The method of claim 3, wherein the skill object descriptor conforms to one of
the object descriptors of the sub set. (See paragraph 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…9. (Original) The method of claim 1, wherein processing the vision data instances to identify the regions of interest in the environment and to determine, for each of the regions of interest, the estimated location and the region embedding comprises: for a given vision data instance of the vision data instances:
processing the given vision data instance, using a class-agnostic object
detection model, to identify a given region of interest in the vision data instance;
determining, based on the given region of interest and a pose of a vision
component when the given vision data instance was captured, the estimated
location for the given region of interest; and
generating the region embedding, for the given region of interest, based on
processing a portion, of the given vision data instance, that corresponds to the
given region of interest, wherein processing the portion is using a visual language
model (VLM) encoder trained for predicting natural language descriptions of
images. (See paragraph 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…10. (Original) The method of claim 1, further comprising, responsive to determining to
implement the robotic skill: (see cleaning function and searching function and receiving tasks by voice in claims 1-10)
processing the subset of object descriptors, the instruction, and the skill
description of the robotic skill, using the LLM, to generate additional LLM output that
models an additional probability distribution, over the candidate word compositions, that
is dependent on the object descriptors, the instruction, and the skill description;
determining, based on the additional LLM output and an additional skill
description that is an additional natural language description of an additional robotic skill
performable by the robot, to implement the additional robotic skill; and
in response to determining to implement the additional robotic skill:
causing the robot to implement the additional robotic skill in the
environment and after implementation of the robotic skill in the environment. (See paragraph 166-171 where the robot can clean or search for different types of objects by word search; see paragraph 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…11. (Original) The method of claim 10, further comprising, responsive to determining to
implement the additional robotic skill: (see paragraph 100-110 where the robot can fly, self drive and clean and/or search for lost items)
processing the subset of object descriptors, the instruction, the skill description of the robotic skill, and the additional skill description of the additional robotic skill, using the LLM, to generate further LLM output that models an additional probability distribution, over the candidate word compositions (see paragraph 99 where the AI can infer the search terms) , that is dependent on the object descriptors, the instruction, the skill description, and the additional skill description; and determining, based on the further LLM output, that performance of the task by the robot is complete. (See paragraph 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…12. (Original) The method of claim 1, further comprising, responsive to determining to
implement the robotic skill: processing the subset of object descriptors, the instruction, and the skill description of the robotic skill, using the LLM, to generate additional LLM output that models an additional probability distribution, over the candidate word compositions, that is dependent on the object descriptors, the instruction, and the skill description; and determining, based on the additional LLM output, that performance of the task by the robot is complete. (See paragraph 99-110 and 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…13. (Original) The method of claim 1, further comprising generating the object descriptor embeddings. (See paragraph 99-110 and 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…14. (Original) The method of claim 13, wherein generating each of the object descriptor
embeddings comprises: processing a corresponding one of the object descriptors, using a text encoding model, to generate a corresponding one of the object descriptor embeddings. (See paragraph 99-110 and 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…15. (Original) The method of claim 1, wherein the object descriptors include one or more object descriptors that are not explicitly specified in the instruction”. (See paragraph 111).
Chae discloses “…16. (Original) The method of claim 15, wherein determining, based on the instruction, object descriptors that each describe a corresponding candidate environmental object relevant to performance of the task comprises: processing the instruction, using the LLM or an additional LLM, to generate alternate LLM output; and determining one or more of the object descriptors based on the alternate LLM output. (See paragraph 99-110 and 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…17. (Original) The method of claim 16, further comprising:
determining, based on the alternate LLM output, a category descriptor of a
category; and determining given descriptors, of the object descriptors, based on the given descriptors being descriptors of specific objects that are members of the category and based on the category descriptor being determined based on the alternate LLM output”. (See paragraph 150-170)
Chae discloses “…18. (Original) The method of claim 16, further comprising:
identifying a category descriptor, of a category, that is present in the instruction; and determining given descriptors, of the object descriptors, based on the given descriptors being descriptors of specific objects that are members of the category and based on the category descriptor being present in the instruction. (See paragraph 150-169 and 99-110 and 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
Chae discloses “…19. (Original) The method of claim 1, wherein determining, based on the LLM output and the skill description that is the natural language description of the robotic skill, to implement the robotic skill, comprises:
determining that the probability distribution, of the LLM output, indicates the skill description with a probability that satisfies a threshold degree of probability and that the probability is greater than other probabilities determined for other candidate skill descriptions of other candidate robotic skills performable by the robot. (See paragraph 99-110 and 32-40 where the object size and shape are associated with the object that is searched using the ai model and see paragraph 271-284 where the robot using the ai engine will search multiple rooms in the home including the living room or dining room etc and see paragraphs 200-229 where the robot can receive the spoken utterance to find the remote and correlate that with the remote control and the optical sensor can search the living room area in FIG. 12 and then note when they have found the remote control and then output the remote control location on the user’s cell phone in a map display with icons) (see paragraph 147 to 170 where the robot has a microphone and an optical sensor and the user can provide a spoken word to find the remote and then the robot can recognize the word and then correlate that to the object and then search for the remote using the sensor and the AI mapping of the environment)
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, 22 and 28 are rejected under 35 U.S.C. sec. 101 as being directed to an abstract idea.
Step 1: Are the claims are directed to an article, machine, manufacturing an article or a process. Claims 1 and 22 and 29 are directed to implementing a method. Claim 22 recites “…22. (Original) A method, comprising: generating, based on processing vision data instances that were captured throughout an environment of one or more robots: regions of interest and, for each of the regions of interest, an estimated map location and a corresponding region embedding; receiving a free form (FF) natural language (NL) instruction that is provided via one or more user interface input devices and that instructs a robot to perform a task; determining, based on the FF NL instruction and the region embeddings for the regions of interest, object descriptors that each describe objects that are relevant to performing the task and that are likely present in the environment; and
utilizing the determined object descriptors in determining robotic skills for at least one of the robot(s) to implement in performing the task”.
This is a statutory class of subject matter. The claims recite a computer user interface in all of claim 1, 22 and 28.
Step 2: The claims recited an abstract idea. Abstract ideas can be grouped as a mathematical concept, a mental process, and certain methods of organizing human activity can be an abstract idea. The claims recite digitizing an instruction and then outputting this instruction to a robot using a natural language model. This is an abstract idea. A human can press a button to activate the task that is desired instead of a computer parsing a phrase and then replacing that to perform the task. The only element seen is a computer interface that can provide a signal to a third party.
Step 2a: Do the claims have an integration into a practical application, or an additional element or combination that imposes a meaningful limit of the judicial exception such that the claim is more than a drafting effort to monopolize the exception. These can include improvements to a technology, reducing or transforming an article to a different state or thing (MPEP 2106.05c), applying or using the judicial exception in some meaningful way beyond linking the use to a particular technological environment such that the claim is more than a drafting effort to monopolize the exception (see MPEP 2106.05€ and Vanda memorandum). Also limitations that are not indicative include adding the words apply it, or more mere instructions to implement the abstract idea on a computer, adding insignificant extra solution activity or a general linking or a field of use or a technological environment. The claims do not recite any practical application. Using a natural language model to control a drone or robot to conduct a task is very well known in the art.
Step 2b is the combination of the steps unconventional? Limitations of an inventive concept include improvements to the functioning of a computer, applying using a machine with the judicial exception, reducing the article to a different thing, applying the judicial exception in a meaningful way.
Improvements can pertain to improvement in the functioning of the computer itself or a computer functionality.
Well understood, routine and conventional changes are excluded under step 2a. See MPEP 2106.04(a).
Improvements can be found in McRo where animation tasks were held to be improvements v. Affinity labs. The feature that leads to the improvement must be in the claim.
A claim limitation can integrate a judicial exception by implementing the exception with a particular machine or manufacture that is integral to the claim. See MPEP 2106.05(b). A generic computer that is specifically programmed does not automatically overcome the exception and it must integrate the judicial exception.
A claim limitation can integrate a judicial exception by using or applying the judicial exception beyond general linking the use to a particular technological environment such that the claim as a whole is more than a drafting effort designed to monopolize the invention.
(For Example 37 of the USPTO Guidance: A method rearranging icons on a GUI by tracking the icons are selected or the amount of memory allocated to the icon, and then ranking the icons, and then placing those icons that are the most used next to or closest to the start icon of the computer based on the amount of use. Step 1 does this claim fall into a statutory category? Yes, the claim recites a method and a series of steps and is a process. Step 2A prong 1: is there a judicial exception recited and the specific limitations and if they are within the groupings of abstract ideas within the claim. Yes, the claims are directed to an abstract idea, of a method of organizing human activity or a mental process or a process of a concept in the human mind. The nominal recitation of a processor does not take it out of the mental process grouping. Step 2A prong 2. Is the judicial exception or combination provided claimed in a manner that provides meaningful limits on the judicial exception that is more than an attempt to draft around the judicial exception. Are they integrated into a practical application of the improvement. Yes, as a whole the mental process is integrated into a practical application of the mental process. Therefore, the claim is eligible versus performing Step 2B as there is no inventive concept recited).
For example, Example 38, organizing patient records, Step 1: the claim is directed to a process and a series of steps; Step 2A it recites an abstract idea of organizing activity. Step 2A prong two: Is there any additional element or combination of elements that recite more than the judicial exception or is more than an attempt to draft around the judicial exception. The claims recite 1. Storing, 2. Remote access, 3. Converting by a content server, automatically generating a message, and transmitting data. This combination of additional elements integrate the abstract idea into a practical application and the combination of elements recite an improvement over the prior art systems by allowing remote systems to share information in real time in a standardized format. Step 2A is no and the claim is eligible).
For example, example 38 claim 2 step 2a prong one the claims recite 1. Storing providing access and messaging. This is a method of organizing human activity. The claims recite performance of claim limitations using generic computer components but does not preclude the claim limitation from being in the certain methods of organizing human activity; this claim 2 recites an abstract idea; Step 2a, prong 2; are there any additional elements that apply on or rely on the judicial exception in a manner that provides meaningful limitations? The claims recite storing information on a memory in a network based storage devices. The claim as a whole does not integrate the abstract idea into a practical application as they do not impose meaningful limits on the abstract idea as the components are at a high level of generality. Step 2B are there elements or combination of elements that are more than the abstract of idea. The claims recite networked memory. This is implementing the abstract idea on a generic computer. Step 2B is no the claim does not provide the inventive concept and is not significantly more than the abstract idea and the claim is not eligible).
The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-20 is/are directed to the general idea of tracking a position of two objects. The method is directed to tracking a position of a user and then tracking a position of a vehicle and then determining when they are within a predetermined distance and then selecting a driver for the user based on the location data. This is a mere abstract idea that is being applied on a general purpose computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only elements found are general purpose computers which is not significantly more than the abstract idea. The claim is directed to a movement vector and a second movement vector and then a matching of the coordinates to select one vehicle from another. This is an abstract idea. This may be performed mathematically or using a pen and paper and using the x, y, z, coordinates of the user and then a number of x, y, z coordinates of a number of vehicles and then seeing which is the closest based on a movement of each. Then a selection may be made based on the distance and time and speed.
The next step is to determine what elements are significantly more than the abstract idea. The only element found is a general purpose computer that displays a map. The arrival time data is input into is part of a general purpose computer in claim 8, and 15 and in claim 1 there is not even a general purpose computer being claimed (it is in the premable). See Alice and Electric Power Group v Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016) that recites “the claims in this case fall into a familiar class of claims “directed to” a patent-ineligible concept. The focus of the asserted claims, as illustrated by claim 12 quoted above, is on collecting information, analyzing it, and displaying certain results of the collection and analysis. We need not define the outer limits of “abstract idea,” or at this stage exclude the possibility that any particular inventive means are to be found somewhere in the claims, to conclude that these claims focus on an abstract idea— and hence require stage-two analysis under § 101.
Information as such is an intangible. See Microsoft Corp. v. AT & T Corp., 550 U.S. 437, 451 n.12 (2007); Bayer AG v. Housey Pharm., Inc., 340 F.3d 1367, 1372 (Fed. Cir. 2003). Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011). In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. See, e.g., TLI Commc’ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App’x 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); see also Mayo, 132 S. Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589–90 (1978); Gottschalk v. Benson, 409 U.S. 63, 67 (1972). And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014)”.
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/JEAN PAUL CASS/Primary Examiner, Art Unit 3666
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