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 .
Claims 1-28 are pending for examination. Claims 1, 16, and 20 are independent.
Priority
Acknowledgement is made of applicant’s claim to U.S. provisional application 63/227,355 filed
on 07/30/2021 for domestic benefit under 35 U.S.C. 119 (e).
Information Disclosure Statement
The information disclosure statements (IDS) were filed on 01/30/2024 and 06/02/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because:
Fig. 2: Feature 208 is not illustrated in the form of a graphical drawing symbol nor a labeled representation (e.g., a labeled rectangular box) — see 37 CFR 1.83(a).
Fig. 3: Specification [0058] describes “sensor device adapter 308” in Figure 3, which is not reflected in Figure 3 of the drawings.
Fig. 4: User computer 214 in the drawing is referred to as User computer 414 within the specification [0056, 0058, 0060, 0062, 0070] — see 37 CFR 1.84(p)(5).
Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because the abstract is 151 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP §608.01(b).
Claim Objections
Claims objected to because of the following informalities:
Claim 6: “wherein said presenting and/or said training includes obtaining”
Claim 6 recites the limitation “including obtaining, using one or more visual and/or audio sensors or said visual and/or audio data.” Examiner is unsure whether this limitation should be interpreted as “using one or more sensors to obtain said data” or “using one or more sensors or said data to obtain a separate, unidentified material”.
Claim 9: “wherein one or more of said unusable portions are not capable of being used for carrying out said training;”
Claim 12: “further comprising: implementing said final model”
Claim 13: remove comma in “said deploying, said deployable model”
Claims 26-27: “further comprises”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following claims contain indefinite language:
Claim 17 recites the limitation “said conveying is carried out after said deeming”. There are two separate recitations of the limitation “deeming” in the referenced Claim 16. For the purpose of examination, Examiner interprets “said deeming” to refer to “deeming said deployable model as final model” per Claim 16.
There is insufficient antecedent basis for the limitations in the following claims.
Claim 12 recites the limitations “said location of interest” and "said human, said animal and/or said plant life". For the purpose of examination, Examiner interprets “said location of interest” as the location where the audio and/or visual data is collected, where the AI adapter device is located, and where the action is taken. “said human, said animal and/or said plant life” is interpreted as human, animal, and/or plant life located at the area of interest.
Claim 14 recites the limitation “said memory”, Examiner is unsure whether this refers to the previously recited “remote memory” or if it lacks antecedent basis. For the purpose of examination, Examiner interprets “said memory” to refer to “remote memory”.
Claim 22 recites the limitations “said mathematical model” and “one or more said data attributes”. For the purpose of examination, Examiner interprets “said mathematical model” as the “customized model” at an initial state prior to training and arriving at a “candidate model” state. “one or more said data attributes” is interpreted to refer to data attributes collected from “said audio sensor and/or said visual sensor” per Claim 20.
Claims 23-25 recite the limitation “said communication component”. For the purpose of examination, Examiner interprets “said communication component” to refer to a component of the AI processor recited in Claim 20 that communicates the audio and/or visual data between the AI processor and a remote database.
Claim 26 recites the limitation “said printed circuit board”. For the purpose of examination, Examiner interprets “said printed circuit board” to refer to “a single printed circuit board” per Claim 23.
Claim 27 recites the limitations “said deployable model” and “said location of interest”. For the purpose of examination, Examiner interprets “said deployable model” to refer to the deployable model per Claim 22. “said location of interest” is interpreted as the location where the audio and/or visual data is collected, where the AI adapter device is located, and where the action is taken.
Claim 28 recites the limitations “said housing” and “said printed circuit board”. For the purpose of examination, Examiner interprets “said housing” as a housing designed to contain the components of the audio/visual data processing device per Claim 20. “said printed circuit board” is interpreted as referring to “a single printed circuit board” per Claim 23.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 8 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 8 recites the limitation "wherein said changing includes producing a portion, and not entire, of said relevant data. The referenced Claim 7 already recites the limitation "changing... to produce at least a portion of said relevant data.". Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-19, 22, and 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim is a process, machine, manufacture, or composition of matter.
In the instant application, Claims 1-15 are directed to a process, Claims 16-19 are directed to a process, and Claims 20-28 are directed to a machine. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
With regard to Claim 1:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
A process for allowing a user to automatically create a customized model that permits an inference, said process comprising:
determining whether said candidate model satisfies one or more predefined model statistics; (This step of determining whether a model satisfies predefined model statistics is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
deeming said candidate model as a deployable model if said candidate model satisfies one or more of said predefined model statistics. (This step of deeming a model to be deployable based on satisfying a statistic is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
2A Prong 2: The judicial exception is not integrated into a practical application.
presenting a plurality of selectable predefined models on a user interface associated with a user computer, wherein each of said selectable predefined model is created using visual and/or audio data; (Presenting information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
receiving, at said user computer, selection of a selected predefined model from plurality of said selectable predefined models; (Receiving information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
making available, on said user interface, selected audio/visual data that was used to create said selected predefined model, such that said selected audio/visual data is capable of being sorted based upon different data attributes; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
receiving, at said user computer, identification of one or more relevant data and/or one or more relevant data attributes that allows sorting and selecting of relevant data from said selected audio/visual data and allows sorting and selecting of one or more of said relevant data attributes from said different data attributes; (Receiving information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
training, using said relevant data and/or said relevant data attributes, said selected predefined model to arrive at a candidate model; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra-solution activities in combination with generic implementation of a model and computer component as a tool to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
presenting a plurality of selectable predefined models on a user interface associated with a user computer, wherein each of said selectable predefined model is created using visual and/or audio data; (Presenting information is understood as a well-understood, routine, and conventional function, being exemplary of transmitting data — see MPEP 2106.05(d)(II)(i).)
receiving, at said user computer, selection of a selected predefined model from plurality of said selectable predefined models; (Receiving information is understood as a well-understood, routine, and conventional function, being exemplary of receiving data — see MPEP 2106.05(d)(II)(i).)
making available, on said user interface, selected audio/visual data that was used to create said selected predefined model, such that said selected audio/visual data is capable of being sorted based upon different data attributes; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
receiving, at said user computer, identification of one or more relevant data and/or one or more relevant data attributes that allows sorting and selecting of relevant data from said selected audio/visual data and allows sorting and selecting of one or more of said relevant data attributes from said different data attributes; (Receiving information is understood as a well-understood, routine, and conventional function, being exemplary of receiving data — see MPEP 2106.05(d)(II)(i).)
training, using said relevant data and/or said relevant data attributes, said selected predefined model to arrive at a candidate model; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not recite significantly more than a judicial exception as they are well-understood, routine, and conventional activities previously known to the industry in combination with generic implementation of a model and computer component as a tool to perform the abstract idea above.
With regard to Claim 2:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, further comprising repeating at least two of said receiving selection of said predefined model, said making available, said receiving of one or more of said relevant data and/or one or more of said relevant data attributes, said training to arrive at said candidate model and said determining whether said candidate model satisfies one or more of said predefined model statistics, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model. (Repeating experimental steps is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, further comprising repeating at least two of said receiving selection of said predefined model, said making available, said receiving of one or more of said relevant data and/or one or more of said relevant data attributes, said training to arrive at said candidate model and said determining whether said candidate model satisfies one or more of said predefined model statistics, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model. (Repeating experimental steps is understood as a well-understood, routine, and conventional function, being exemplary of performing repetitive calculations — see MPEP 2106.05(d)(II)(ii).)
With regard to Claim 3:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said presenting of plurality of said selectable predefined models includes presenting on an Internet website or a software application interface that is generated at said user computer. (Presenting information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said presenting of plurality of said selectable predefined models includes presenting on an Internet website or a software application interface that is generated at said user computer. (Presenting data is understood as a well-understood, routine, and conventional function, being exemplary of presenting offers and gathering statistics— see MPEP 2106.05(d)(II)(iv).)
With regard to Claim 4:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said receiving identification of one or more of said relevant data attributes includes receiving at least one attribute chosen from a group comprising date of creation of said relevant data, time of creation of said relevant data, location coordinates of location from where said relevant data was retrieved, species involved in said relevant data and animal present in said relevant data. (The specification of the received data is understood to be a field of use limitation — see MPEP 2106.05(h).)
With regard to Claim 5:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said training includes using situational awareness bias attributes to identify said candidate model, and wherein said situational awareness bias attributes include at least one attribute chosen from a group comprises geographical data of said relevant data, temporal data of said relevant data, weather conditions during retrieval of said relevant data, and previous inferences drawn from said relevant data. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
With regard to Claim 6:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said presenting and/or said training including obtaining, using one or more visual and/or audio sensors or said visual and/or audio data. (Obtaining data is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said presenting and/or said training including obtaining, using one or more visual and/or audio sensors or said visual and/or audio data. (Obtaining data is understood as a well-understood, routine, and conventional function, being exemplary of presenting offers and gathering statistics— see MPEP 2106.05(d)(II)(iv).)
With regard to Claim 7:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
changing, based upon said operational instructions, operating conditions of said visual and/or audio sensors for collecting said relevant data to produce at least a portion of said relevant data. (This step of changing operating conditions is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 6 of allowing a user to automatically create a customized model that permits an inference, further comprising:
conveying operational instructions pertinent to one or more of said relevant data attributes to one or more controllers that control operation of said visual and/or audio sensors; and (Conveying instructions pertinent to relevant data is understood as an insignificant extra-solution activity, exemplary of selecting a particular data source or type of data to be manipulated — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 6 of allowing a user to automatically create a customized model that permits an inference, further comprising:
conveying operational instructions pertinent to one or more of said relevant data attributes to one or more controllers that control operation of said visual and/or audio sensors; and (Conveying instructions is understood as a well-understood, routine, and conventional function, being exemplary of transmitting information— see MPEP 2106.05(d)(II)(i).)
With regard to Claim 8:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
The process of claim 7 of allowing a user to automatically create a customized model that permits an inference, wherein said changing includes producing a portion, and not entire, of said relevant data. (This step of changing operating conditions is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2 &2B: The claim does not recite any additional elements.
With regard to Claim 9:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
filtering out, using one or more algorithms, unusable portions of said relevant data; and (Filtering data is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said training further comprises:
receiving relevant data that includes one or more usable portions and one or more unusable portions, wherein said usable portion are capable of being used for carrying out said training, and wherein one or more of said unusable portions are not capable of being used for carrying out of said training; and (Receiving data is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
filtering out, using one or more algorithms, unusable portions of said relevant data; and (Training a model is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
training said selected predefined model using one or more usable portions of said relevant data. (Using an algorithm is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said training further comprises:
receiving relevant data that includes one or more usable portions and one or more unusable portions, wherein said usable portion are capable of being used for carrying out said training, and wherein one or more of said unusable portions are not capable of being used for carrying out of said training; and (Receiving/transmitting information is understood as a well-understood, routine, and conventional function, being exemplary of receiving/transmitting data — see MPEP 2106.05(d)(II)(i).)
filtering out, using one or more algorithms, unusable portions of said relevant data; and (Using an algorithm is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
training said selected predefined model using one or more usable portions of said relevant data. (Training a model is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
With regard to Claim 10:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, further comprising:
determining whether said inference satisfies one or more predefined inference criteria; and (This step of determining whether an inference satisfies a criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and (This step of deeming a deployable model to be a final model is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2 & 2B:
deploying said deployable model in said user computer, an AI adapter device, or a remote processor to permit an inference, wherein said remote processor is present at a location remote to a location of said AI adapter device; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. observation/evaluation/judgement/opinion) — see MPEP 2106.05(f).)
modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. observation/evaluation/judgement/opinion) — see MPEP 2106.05(f).)
With regard to Claim 11:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 10 of allowing a user to automatically create a customized model that permits an inference, further comprising conveying said deployable model and/or audio and/or visual data associated with said deployable model from said user computer or said remote processor to said AI adapter device, if said deeming is carried out by said user computer or said remote processor. (Conveying data is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 10 of allowing a user to automatically create a customized model that permits an inference, further comprising conveying said deployable model and/or audio and/or visual data associated with said deployable model from said user computer or said remote processor to said AI adapter device, if said deeming is carried out by said user computer or said remote processor. (Receiving/transmitting information is understood as a well-understood, routine, and conventional function, being exemplary of receiving/transmitting data — see MPEP 2106.05(d)(II)(i).)
With regard to Claim 12:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
The process of claim 11 of allowing a user to automatically create a customized model that permits an inference, further:comprising
implementing said final model on said AI adapter device; and (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f)(1)(iii).)
taking an action, using said AI adapter device, at said location of interest to conserve said human, said animal and/or said plant life. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f)(1)(iii).)
With regard to Claim 13:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
The process of claim 11 of allowing a user to automatically create a customized model that permits an inference, wherein in said deploying, said deployable model is stored on a remote memory accessible by said remote processor, and wherein said remote memory is present at a location remote to said location of said AI adapter device. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
With regard to Claim 14:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 13 of allowing a user to automatically create a customized model that permits an inference, wherein said conveying includes conveying from said memory accessible by said user computer or said remote memory accessible by said remote processor to said AI adapter device. (Conveying data is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 13 of allowing a user to automatically create a customized model that permits an inference, wherein said conveying includes conveying from said memory accessible by said user computer or said remote memory accessible by said remote processor to said AI adapter device. (Conveying data is understood as a well-understood, routine, and conventional function, being exemplary of receiving or transmitting data — see MPEP 2106.05(d)(II)(i).)
With regard to Claim 15:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of claim 14 of allowing a user to automatically create a customized model that permits an inference, wherein said conveying said final model includes conveying final data and/or final data attributes underlying said final model, wherein said final data includes one or more new data not present in said relevant data and/or does not include one or more excised data that were present in said relevant data, and said final data attributes includes one or more new data attributes not present in said relevant data attributes and/or does not include one or more excised data attributes that were present in said relevant data attributes. (Conveying data is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of claim 14 of allowing a user to automatically create a customized model that permits an inference, wherein said conveying said final model includes conveying final data and/or final data attributes underlying said final model, wherein said final data includes one or more new data not present in said relevant data and/or does not include one or more excised data that were present in said relevant data, and said final data attributes includes one or more new data attributes not present in said relevant data attributes and/or does not include one or more excised data attributes that were present in said relevant data attributes. (Conveying data is understood as a well-understood, routine, and conventional function, being exemplary of receiving or transmitting data — see MPEP 2106.05(d)(II)(i).)
With regard to Claim 16:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
A process for facilitating environmental conservation comprising:
creating a mathematical model, using one or more data and/or one or more data attributes and that is describing a phenomenon involving a human, animal, and/or plant presence or behavior at a location of interest; (The mathematical model is defined in applicant specification [0069-0075] as being created by relevant data in order to predict or determine an environmental action ([0042-0043]) and is understood as a mathematical concept (i.e. mathematical relationship) — see MPEP 2106.04(a)(2)(I)(A)(iv).)
determining whether said candidate model satisfies one or more predefined model statistics; (This step of determining whether a model satisfies predefined model statistics is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
deeming said candidate model as said deployable model if said candidate model satisfies one or more of said predefined model statistics; (This step of deeming a model to be deployable based on satisfying a statistic is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
determining whether said inference satisfies one or more predefined inference criteria; (This step of determining whether an inference satisfies predefined inference criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model; (This step of deeming a model to be final based on satisfying a criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
2A Prong 2: The judicial exception is not integrated into a practical application.
training said mathematical model to arrive at a candidate model using said data, new data, one or more said data attributes and/or one or more new data attributes; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
repeating said creating, said training and said determining, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model; and (Repeating experimental steps is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
deploying said deployable model on said user computer, an AI adapter device, and/or a remote processor to permit an inference; wherein said user computer and said remote processor are present at a location that is remote to location of said AI adapter device; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
implementing said final model on said AI adapter device to draw an inference; and (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
taking an action, using said AI adapter device, at said location of interest to conserve said human, said animal and/or said plant life. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
training said mathematical model to arrive at a candidate model using said data, new data, one or more said data attributes and/or one or more new data attributes; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. observation/evaluation/judgement/opinion) — see MPEP 2106.05(f).)
repeating said creating, said training and said determining, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model; and (Repeating experimental steps is understood as a well-understood, routine, and conventional function, being exemplary of performing repetitive calculations — see MPEP 2106.05(d)(II)(ii).)
deploying said deployable model on said user computer, an AI adapter device, and/or a remote processor to permit an inference; wherein said user computer and said remote processor are present at a location that is remote to location of said AI adapter device; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
implementing said final model on said AI adapter device to draw an inference; and (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
taking an action, using said AI adapter device, at said location of interest to conserve said human, said animal and/or said plant life. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
With regard to Claim 17:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
The process of facilitating environmental conservation of claim 16, further comprising conveying said final model to an AI adapter device, if said final model resides on said user computer and/or said remote processor, and wherein said conveying is carried out after said deeming and prior to said implementing.. (Conveying data is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The process of facilitating environmental conservation of claim 16, further comprising conveying said final model to an AI adapter device, if said final model resides on said user computer and/or said remote processor, and wherein said conveying is carried out after said deeming and prior to said implementing. (Conveying data is understood as a well-understood, routine, and conventional function, being exemplary of receiving or transmitting data — see MPEP 2106.05(d)(II)(i).)
With regard to Claim 18:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
The process of facilitating environmental conservation of claim 16, wherein said training, said determining and said deeming are carried out at said user computer or said processor present at said location that is remote to said location of said AI adapter device. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
With regard to Claim 19:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
The process of facilitating environmental conservation of claim 16, wherein said taking an action includes one action chosen from a group comprising sending a notification to said user computer and/or a third party, setting a trap, sounding an alarm, recording an image or a video, recording a sound, depleting resources consumed by an invasive animal or a plant species, dispersing food, monitoring animal or plant health, and administering medicine or vaccine. (This step of monitoring is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. observation).)
2A Prong 2 & 2B:
The process of facilitating environmental conservation of claim 16, wherein said taking an action includes one action chosen from a group comprising sending a notification to said user computer and/or a third party, setting a trap, sounding an alarm, recording an image or a video, recording a sound, depleting resources consumed by an invasive animal or a plant species, dispersing food, monitoring animal or plant health, and administering medicine or vaccine. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
With regard to Claim 22:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
determining whether said candidate model satisfies one or more predefined model statistics; (This step of determining whether a model satisfies predefined model statistics is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
deeming said candidate model as said deployable model if said candidate model satisfies one or more of said predefined model statistics; (This step of deeming a model to be deployable based on satisfying a statistic is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
determining whether said inference satisfies one or more predefined inference criteria; (This step of determining whether an inference satisfies predefined inference criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model; (This step of deeming a model to be final based on satisfying a criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
2A Prong 2: The judicial exception is not integrated into a practical application.
The audio/visual data processing device of Claim 20 comprising: an audio sensor and/or a visual sensor; an AI adapter device comprising: an audio controller and/or a visual controller that is designed to control operation of said audio sensor and/or said visual sensor; an AI processor for processing data collected from said audio sensor and/or said visual sensor; and a power source for powering said AI processor. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
further comprising a user computer or a remote processor having programmed thereon instructions for allowing a user to automatically create a customized model that permits an inference and/or deployment of said customized model to permit said inference, wherein a memory accessible by said user computer or a remote memory accessible by said remote processor has stored thereon instructions for: (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
training said mathematical model to arrive at a candidate model using said data, new data, one or more said data attributes and/or one or more new data attributes; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
repeating said creating, said training and said determining, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model; and (Repeating experimental steps is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
deploying said deployable model to permit an inference; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
conveying said final model to said AI adapter device. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The audio/visual data processing device of Claim 20 comprising: an audio sensor and/or a visual sensor; an AI adapter device comprising: an audio controller and/or a visual controller that is designed to control operation of said audio sensor and/or said visual sensor; an AI processor for processing data collected from said audio sensor and/or said visual sensor; and a power source for powering said AI processor. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
further comprising a user computer or a remote processor having programmed thereon instructions for allowing a user to automatically create a customized model that permits an inference and/or deployment of said customized model to permit said inference, wherein a memory accessible by said user computer or a remote memory accessible by said remote processor has stored thereon instructions for: (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
training said mathematical model to arrive at a candidate model using said data, new data, one or more said data attributes and/or one or more new data attributes; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. observation/evaluation/judgement/opinion) — see MPEP 2106.05(f).)
repeating said creating, said training and said determining, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model; and (Repeating experimental steps is understood as a well-understood, routine, and conventional function, being exemplary of performing repetitive calculations — see MPEP 2106.05(d)(II)(ii).)
deploying said deployable model to permit an inference; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
implementing said final model to said AI adapter device. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
With regard to Claim 27:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
determining whether said inference satisfies one or more predefined inference criteria; (This step of determining whether an inference satisfies predefined inference criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model; (This step of deeming a model to be final based on satisfying a criteria is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. opinion).)
2A Prong 2: The judicial exception is not integrated into a practical application.
The audio/visual data processing device of Claim 20 comprising: an audio sensor and/or a visual sensor; an AI adapter device comprising: an audio controller and/or a visual controller that is designed to control operation of said audio sensor and/or said visual sensor; an AI processor for processing data collected from said audio sensor and/or said visual sensor; and a power source for powering said AI processor. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
wherein said AI processor further comprising an AI adapter device memory having stored thereon instructions for: (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
deploying said deployable model to permit an inference; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
implementing said final model to draw an inference; and (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
taking an action at said location of interest. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The audio/visual data processing device of Claim 20 comprising: an audio sensor and/or a visual sensor; an AI adapter device comprising: an audio controller and/or a visual controller that is designed to control operation of said audio sensor and/or said visual sensor; an AI processor for processing data collected from said audio sensor and/or said visual sensor; and a power source for powering said AI processor. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
wherein said AI processor further comprising an AI adapter device memory having stored thereon instructions for: (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
deploying said deployable model to permit an inference; (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
implementing said final model to draw an inference; and (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. evaluation) — see MPEP 2106.05(f).)
taking an action at said location of interest. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(f).)
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)(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 20-22 and 24-27 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Haemel et al. (US 2020/0027210 A1).
With regard to Claim 20:
Haemel teaches: An audio/visual data processing device comprising:
an audio sensor and/or a visual sensor; ([0002, 0020] Haemel discusses different imaging devices used to aid users in visualization tasks, including RADAR, SONAR, and LIDAR. The imaging machines, RADAR, and LIDAR are understood as visual sensors, while SONAR is understood as an audio sensor. [0034] Haemel discloses imaging devices that are visual sensors. [0112] Haemel discloses “the presentation component(s) 818 may receive data from other components and output the data (e.g., as an image, video, sound, etc.).”, meaning the other components may be understood as audio and/or visual sensors that provide audio and/or visual data.)
an AI adapter device comprising: an audio controller and/or a visual controller that is designed to control operation of said audio sensor and/or said visual sensor; ([0073] Haemel discloses using an exposure control AI or AI controller to control the exposure of a CT scanner. [0061] Haemel states that the CT scanner may generate imaging data. Applicant specification [0047] describes a sensor device as “any data collection device, sensor device, or data logger used to collect data at a location of interest… sensor device 104 collects at least one member selected from a group comprising image data, video data, and acoustic data.” [0034] Haemel discloses that the hardware may contain GPUs and graphics cards that are understood as visual controllers.)
an AI processor for processing data collected from said audio sensor and/or said visual sensor; and ([0034] Haemel states “the cloud platform may be executed using an AI/deep learning supercomputer(s),” [0056-0058] Haemel discusses the how the AI system may use GPUs and may be implemented in the cloud “for performing some or all of the AI-based processing tasks of the system.” [0021] Haemel recites “at least some of the processing and compute resources may be offloaded to the cloud, or to AI systems specifically designed for handling the application and/or service processing of the pipeline,” further exemplifying how the AI processing may occur remotely.)
a power source for powering said AI processor. ([0100] Haemel discloses that the computing device may include a power supply. [0067] Haemel recites that the computing system “may include some or all of the hardware 122,” [0055] Haemel recites “the hardware 122 may include GPUs 222, the AI system 224, the cloud 226, and/or any other hardware used for executing the training system 104 and/or the deployment system 106.”)
With regard to Claim 21:
Haemel teaches: The audio/visual data processing device of claim 20, further comprising a connecting component communicatively connecting said audio sensor and/or said visual sensor to said AI adapter device. ([Fig. 8, [0110]] Haemel recites “The I/O ports 812 may enable the computing device 800 to be logically coupled to other devices including the I/O components 814,… I/O components 814 include a microphone… satellite dish, scanner, wireless device, etc.” which is interpreted to be a communicative connection between a sensor and a computing or AI adapter device.)
With regard to Claim 22:
Haemel teaches: The audio/visual data collection device of claim 20, further comprising a user computer or a remote processor having programmed thereon instructions for allowing a user to automatically create a customized model that permits an inference and/or deployment of said customized model to permit said inference, wherein a memory accessible by said user computer or a remote memory accessible by said remote processor has stored thereon instructions for: ([0092] Haemel recites “various functions may be carried out by a processor executing instructions stored in memory. Furthermore, Haemel recites in the same paragraph “the method 700 may be provided by a standalone application, a service, or hosted service,” in which the hosted service is interpreted to represent a remote processor or remote memory. [0046] Haemel describes a user interface “that may be used to select applications for inclusion in the deployment pipeline(s) 210, arrange the applications, modify or change the applications or parameters or constructs thereof.”)
training said mathematical model to arrive at a candidate model using said data, new data, one or more said data attributes and/or one or more new data attributes; ([0086] Haemel discusses using the associated customer dataset for model training to generate a refined model, wherein the associated customer dataset would be the one or more new data/data attributes.)
determining whether said candidate model satisfies one or more predefined model statistics; ([0089] Haemel discusses updating the model parameters until it satisfies a level of accuracy, and refining it on new datasets in order to generate a more universal model that may satisfy additional model statistics.)
repeating said creating, said training and said determining, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model; ([0089-0090] Haemel discloses using the dataset to train and update the model “any number of times” until an accuracy threshold is attained and a more universal model is generated.)
deeming said candidate model as said deployable model if said candidate model satisfies one or more of said predefined model statistics; ([0089] Haemel recites “the ground truth data may be used to update the parameters of the initial model 604 until an acceptable level of accuracy is attained for the refined model.” Applicant specification [0082] discloses that predefined model statistics may include accuracy.)
deploying said deployable model to permit an inference; ([0089] Haemel recites that the initial model is trained to generate the refined model, and [0090] “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124” to perform “one or more processing tasks” [0025] Haemel recites “the model registry 124 may include machine learning models trained to perform a variety of different inference tasks on imaging data.” This is interpreted as reciting how the refined or deployed model in the model registry may perform an inference task or permit an inference.)
determining whether said inference satisfies one or more predefined inference criteria; ([0083] Haemel recites the model training process, wherein “the parameters may be updated and re-tuned for a new data set based on loss calculations associated with the accuracy of the output or loss layer(s) at generating predictions on the new, customer dataset 606.” This is interpreted as the loss calculations or output accuracy checking whether the output or inference of the model satisfies a required accuracy or inference criteria.)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model; ([0089] Haemel recites that the initial model is trained to generate the refined model “until an acceptable level of accuracy is attained,” and [0090] Haemel recites “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” “the refined model 612 may be further refined on new datasets any number of times to generate a more universal model.” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, then that 1st refined model being further refined into a final or more universal model. The refined or deployable model being able to be refined “any number of times” is interpreted as being modified until a universal model is generated or the inference criteria is satisfied to produce the final model, as the refining process is understood to be the same as the process described in Haemel [0089].)
conveying said final model to said AI adapter device. ([0089-0090] Haemel recites a “refined model” or final model that is “deployed within one or more deployment pipelines 210” for “performing one or more processing tasks.” [0021] Haemel recites “deployment system 106” involved in the “inferencing pipeline” and configured to execute services of “inference, visualization, compute, AI, etc.” [0044, 0046] Haemel recites how the “deployment system 106” is applied to “interact with the deployment pipeline(s) 210, in which the deployment system is interpreted as an AI adapter or system.)
With regard to Claim 24:
Haemel teaches: The audio/visual data collection device of claim 20, wherein said AI processor is a central processing unit that has disposed thereon said communication component which serves to establish a wireless local area network connection. ([0100] Haemel recites an “example computing device 800” wherein the one or more CPUs may be directly coupled to the communication interface by “bus 802”. [0109] Haemel recites the details of the communication interface in which it may “enable communication over any of a number of different networks, such as wireless networks” and “low-power wide-area networks”. Wireless networks are exemplified as “Z-Wave, Bluetooth, Bluetooth LE, Zigbee, etc.” which are interpreted to be local area wireless connections.)
With regard to Claim 25:
Haemel teaches: The audio/visual data collection device of claim 20, wherein said communication component is communicatively coupled to a cloud-based database. ([0023] Haemel recites “The object storage may be accessible through, for example, a cloud storage compatible application programming interface (API) from within the cloud platform.” This is interpreted as a cloud-based database that must be communicatively coupled to the communication component of the device in order for the user to interact with the models stored in the model registry.)
With regard to Claim 26:
Haemel teaches: The audio/visual data collection device of claim 20, wherein said printed circuit board further comprising a long-range communication chip for communicating using low-power wide area network, cellular, or satellite communications. ([0100, 0109] Haemel recites “the communication interface 810” coupled to the bus or circuit board that enables wireless communications such as “low-power wide-area networks.” [0110] Haemel recites a “satellite dish” as one of the possible I/O components coupled to the computing device, implying satellite communications.)
With regard to Claim 27:
Haemel teaches: The audio/visual data collection device of claim 20, wherein said AI processor further comprising an AI adapter device memory having stored thereon instructions for: ([0092] Haemel recites “various functions may be carried out by a processor executing instructions stored in memory.)
deploying said deployable model to permit an inference; ([0089] Haemel recites that the initial model is trained to generate the refined model, and [0090] “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124” to perform “one or more processing tasks” [0025] Haemel recites “the model registry 124 may include machine learning models trained to perform a variety of different inference tasks on imaging data.” This is interpreted as reciting how the refined or deployed model in the model registry may perform an inference task or permit an inference.)
determining whether said inference satisfies one or more predefined inference criteria; ([0039] Haemel recites [0083] Haemel recites the model training process, wherein “the parameters may be updated and re-tuned for a new data set based on loss calculations associated with the accuracy of the output or loss layer(s) at generating predictions on the new, customer dataset 606.” This is interpreted as the loss calculations or output accuracy checking whether the output or inference of the model satisfies a required accuracy or inference criteria.)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model; ([0089] Haemel recites that the initial model is trained to generate the refined model “until an acceptable level of accuracy is attained,” and [0090] Haemel recites “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” “the refined model 612 may be further refined on new datasets any number of times to generate a more universal model.” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, then that 1st refined model being further refined into a final or more universal model. The refined or deployable model being able to be refined “any number of times” is interpreted as being modified until a universal model is generated or the inference criteria is satisfied to produce the final model, as the refining process is understood to be the same as the process described in Haemel [0089].)
implementing said final model to draw an inference; and ([0089-0090] Haemel recites a “refined model” or final model that is “deployed within one or more deployment pipelines 210” for “performing one or more processing tasks.” [0021] Haemel recites “deployment system 106” involved in the “inferencing pipeline” and configured to execute services of “inference, visualization, compute, AI, etc.” [0044, 0046] Haemel recites how the “deployment system 106” is applied to “interact with the deployment pipeline(s) 210, in which the deployment system is interpreted as an AI adapter or system.)
taking an action at said location of interest. ([0073] Haemel explains how the pipeline may apply to a CT scanner in the process of monitoring a patient. Haemel describes the output of the AI applications being used as feedback for the technician to take action in “adjusting the exposure (or other settings of the CT scanner 422) and/or informing the patient to move less.” This is interpreted as an action taken at the location of interest as the AI application motivates the technician to act upon the same patient that is being monitored by that AI application.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-3, 6, 9-11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Haemel et al. (US 2020/0027210 A1), hereinafter “Haemel”, in view of Sheffer et al. (US 2016/0085880 A1), hereinafter “Sheffer”.
With regard to Claim 1:
Haemel teaches: A process for allowing a user to automatically create a customized model that permits an inference, said process comprising: ([0048] Haemel discusses how users may develop, modify, and deploy each application of the system. [0050] Haemel mentions that applications may use one or more models to perform inference on imaging data. [Abstract, 0005] Haemel labels this main process as “inference pipeline customization” wherein machine learning models may be trained and updated to be leveraged for inferencing operations.)
presenting a plurality of selectable predefined models on a user interface associated with a user computer, wherein each of said selectable predefined model is created using visual and/or audio data; ([0025-0026, 0083-0084] Haemel discusses situations where a trained or predefined model may be selected from a model registry of predefined models, implying that the model registry is presented to the user prior to selection. [0046] Haemel mentions that the deployment system for the model may include a user interface to select models from the registry.)
receiving, at said user computer, selection of a selected predefined model from plurality of said selectable predefined models; ([0025-0026, 0045, 0085] Haemel discusses situations where a trained or predefined model may be selected from a model registry of predefined models. [Fig. 7, [0093]] Haemel states “The method 700, at block B702, includes receiving an input corresponding to a selection of a neural network from a model registry.” [0086-0088] Haemel discusses how the selected model and its associated data may be fine-tuned by the user, implying that the selected model is received at the user computer.)
training, using said relevant data and/or said relevant data attributes, said selected predefined model to arrive at a candidate model; ([0086-0089] Haemel discusses using the associated data for model training to generate a refined model.)
determining whether said candidate model satisfies one or more predefined model statistics; ([0089-0090] Haemel discusses updating the model parameters until it satisfies a level of accuracy, and refining it on new datasets in order to generate a more universal model that may satisfy additional model statistics.)
deeming said candidate model as a deployable model if said candidate model satisfies one or more of said predefined model statistics. ([0030] Haemel states “Once validated by the system (e.g., for accuracy, safety, patient privacy, etc.), the application may be available in a container registry for selection and/or implementation by a user,” meaning the model is deemed deployable to a userbase after satisfying a statistic such as accuracy or safety.)
Haemel does not explicitly teach: making available, on said user interface, selected audio/visual data that was used to create said selected predefined model, such that said selected audio/visual data is capable of being sorted based upon different data attributes;
receiving, at said user computer, identification of one or more relevant data and/or one or more relevant data attributes that allows sorting and selecting of relevant data from said selected audio/visual data and allows sorting and selecting of one or more of said relevant data attributes from said different data attributes;
However, Sheffer teaches in the same field of endeavor: making available, on said user interface, selected audio/visual data that was used to create said selected predefined model, such that said selected audio/visual data is capable of being sorted based upon different data attributes; ([0005, 0018, 0024] Sheffer describes how the system displays data on a user interface that is sorted on one or more attributes. [0021] Sheffer describes how the data is processed for further metadata that may further enrich the details of the data that are viewable by the user.)
receiving, at said user computer, identification of one or more relevant data and/or one or more relevant data attributes that allows sorting and selecting of relevant data from said selected audio/visual data and allows sorting and selecting of one or more of said relevant data attributes from said different data attributes; ([Fig. 4, [0025-0028]] Sheffer describes how the user is able to select one or more attributes by which to sort the dataset, and that this selection “can be repeated as many times as necessary so that the selection of the records from the dataset is iteratively refinable.”)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Haemel’s teachings by making the relevant data and/or data attributes able to be sorted and selected by the user as taught by Sheffer. One would have been motivated to make this modification in order to improve relevancy of the model to the task or action required of the user and to make larger quantities of data easier to analyze and explore. (Sheffer [0004])
With regard to Claim 2:
Haemel in view of Sheffer teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, further comprising repeating at least two of said receiving selection of said predefined model, said making available, said receiving of one or more of said relevant data and/or one or more of said relevant data attributes, said training to arrive at said candidate model and said determining whether said candidate model satisfies one or more of said predefined model statistics, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model. ([0089-0090] Haemel discloses using the dataset to train and update the model “any number of times” until an accuracy threshold is attained and a more universal model is generated. [0086-0088] Haemel describes how the pre-trained model may be optimized, updated, retrained, and/or fine-tuned with regard to the customer dataset which would be akin to a customized model created for a specific use case.)
With regard to Claim 3:
Haemel in view of Sheffer teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said presenting of plurality of said selectable predefined models includes presenting on an Internet website or a software application interface that is generated at said user computer. ([0029] Haemel discusses how the data processing pipeline that includes storing the trained models in the model registry may be encapsulated in a container that represents a fully functional application instantiation, or a software application. The same paragraph states an example “once selected by a user from the container registry for deployment in a pipeline, the image may be used to generate a container for an instantiation of the application for use by the user’s system.”[0045-0046] Haemel discloses how applications and models may be selectable and customizable by the user through “a user interface (e.g., a graphical user interface, a web interface, and/or the like).”)
With regard to Claim 6:
Haemel in view of Sheffer teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said presenting and/or said training including obtaining, using one or more visual and/or audio sensors or said visual and/or audio data. (Examiner interprets this limitation as “…obtaining said visual and/or audio data using one or more visual and/or audio sensors.” [0027-0028] Haemel discloses how a data processing pipeline may receive input or imaging data from imaging devices to perform inferencing tasks through one or more machine learning models.)
With regard to Claim 9:
Haemel in view of Sheffer teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, wherein said training further comprises:
receiving relevant data that includes one or more usable portions and one or more unusable portions, wherein said usable portion are capable of being used for carrying out said training, and wherein one or more of said unusable portions are not capable of being used for carrying out of said training; and ([0043] Haemel recites “there may be more than one deployment pipeline depending on the information desired from the data generated by the device.” This is interpreted as there being unusable portions of data wherein the deployment pipeline does not require those unusable portions of data for its specific training purposes. [0031] Haemel recites that the user may “browse the container registry and/or the model registry 124 for an application, container, dataset, machine learning model, etc., select the desired combination of elements for inclusion in the data processing pipeline, and submit an imaging processing request,” wherein selecting a desired combination of elements for inclusion in the data processing pipeline is understood as there being usable and unusable portions of the data regarding carrying out the model training.)
filtering out, using one or more algorithms, unusable portions of said relevant data; and ([0032] Haemel recites “A data augmentation service may further be included that may provide GPU accelerated data extraction," in which the GPU accelerated data extraction is interpreted as filtering of data using an algorithm.)
training said selected predefined model using one or more usable portions of said relevant data. ([0031] Haemel discusses how the desired data is selected to be included in the imaging processing request alongside the input data and sent to the machine learning models for processing execution.)
With regard to Claim 10:
Haemel in view of Sheffer teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference, further comprising: deploying said deployable model in said user computer, an AI adapter device, or a remote processor to permit an inference, wherein said remote processor is present at a location remote to a location of said AI adapter device; ([0089] Haemel recites that the initial model is trained to generate the refined model, and [0090] “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, that will be uploaded to the model registry for deployment or further training. [0021] Haemel recites “deployment system 106” that the “process 100” or “training system 104” may be executed upon “to perform training, deployment, and implementation of machine learning models,” and “the pipeline may be use or call upon services (e.g., inference, visualization, compute, AI, etc.) of the deployment system 106 during execution of the applications.” The deployment system is interpreted as a user computer and/or an AI adapter device. [0031] Haemel recites “the request may then be passed to one or more components of the deployment system 106 (e.g., the cloud) to perform the processing of the data processing pipeline,” in which the deployment system or the cloud is interpreted as a remote processor or system.)
determining whether said inference satisfies one or more predefined inference criteria; and ([0039] Haemel recites [0083] Haemel recites the model training process, wherein “the parameters may be updated and re-tuned for a new data set based on loss calculations associated with the accuracy of the output or loss layer(s) at generating predictions on the new, customer dataset 606.” This is interpreted as the loss calculations or output accuracy checking whether the output or inference of the model satisfies a required accuracy or inference criteria.)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model. ([0089] Haemel recites that the initial model is trained to generate the refined model “until an acceptable level of accuracy is attained,” and [0090] Haemel recites “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” “the refined model 612 may be further refined on new datasets any number of times to generate a more universal model.” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, then that 1st refined model being further refined into a final or more universal model. The refined or deployable model being able to be refined “any number of times” is interpreted as being modified until a universal model is generated or the inference criteria is satisfied to produce the final model, as the refining process is understood to be the same as the process described in Haemel [0089].)
With regard to Claim 11:
Haemel in view of Sheffer teaches: The process of claim 10 of allowing a user to automatically create a customized model that permits an inference, further comprising conveying said deployable model and/or audio and/or visual data associated with said deployable model from said user computer or said remote processor to said AI adapter device, if said deeming is carried out by said user computer or said remote processor. ([0031] Haemel recites “the request may then be passed to one or more components of the deployment system 106 (e.g., the cloud) to perform the processing of the data processing pipeline,” in which the deployment system or the cloud is interpreted as a remote processor or system and the request comprising data such as the selected model is made from the user computer.)
With regard to Claim 13:
Haemel in view of Sheffer teaches: The process of claim 11 of allowing a user to automatically create a customized model that permits an inference, wherein in said deploying, said deployable model is stored on a remote memory accessible by said remote processor, and wherein said remote memory is present at a location remote to said location of said AI adapter device. ([0023] Haemel states that the model registry may be accessible through a cloud or remote storage, the storage and retrieval functionality of the cloud storage implies the use of memory.)
With regard to Claim 14:
Haemel in view of Sheffer teaches: The process of claim 13 of allowing a user to automatically create a customized model that permits an inference, wherein said conveying includes conveying from said memory accessible by said user computer or said remote memory accessible by said remote processor to said AI adapter device. ([0031] Haemel recites “the request may then be passed to one or more components of the deployment system 106 (e.g., the cloud) to perform the processing of the data processing pipeline,” in which the deployment system or the cloud is interpreted as a remote processor or system and the request that includes the selected model(s) from the model registry is made from the user computer. Storage and retrieval from the model registry implies the use of memory.)
With regard to Claim 15:
Haemel in view of Sheffer teaches: The process of claim 14 of allowing a user to automatically create a customized model that permits an inference, wherein said conveying said final model includes conveying final data and/or final data attributes underlying said final model, wherein said final data includes one or more new data not present in said relevant data and/or does not include one or more excised data that were present in said relevant data, and said final data attributes includes one or more new data attributes not present in said relevant data attributes and/or does not include one or more excised data attributes that were present in said relevant data attributes. ([0029-0031] Haemel describes the function of a container registry in which a user may store validated or final data of the application within the container registry for further selection and/or implementation. [0049] Haemel describes how new, updated, or modified data may be generated through processing and stored or shared between the applications in the container registry.)
Claims 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Haemel in view of Aldridge et al. (US 10,939,248 B1), hereinafter “Aldridge”.
With regard to Claim 16:
Haemel teaches: A process for facilitating environmental conservation comprising:
creating a mathematical model, using one or more data and/or one or more data attributes ([0025] Haemel discusses how the machine learning models in the model registry may be trained on corresponding data from a specific location or facility.)
training said mathematical model to arrive at a candidate model using said data, new data, one or more said data attributes and/or one or more new data attributes; ([0086] Haemel discusses using the associated customer dataset for model training to generate a refined model, wherein the associated customer dataset would be the one or more new data/data attributes.)
determining whether said candidate model satisfies one or more predefined model statistics; ([0089] Haemel discusses updating the model parameters until it satisfies a level of accuracy, and refining it on new datasets in order to generate a more universal model that may satisfy additional model statistics.)
repeating said creating, said training and said determining, if said candidate model does not satisfy one or more of said predefined model statistics, until said candidate model satisfies one or more of said predefined model statistics to produce a deployable model; and ([0089-0090] Haemel discloses using the dataset to train and update the model “any number of times” until an accuracy threshold is attained and a more universal model is generated.)
deeming said candidate model as said deployable model if said candidate model satisfies one or more of said predefined model statistics; ([0089] Haemel recites “the ground truth data may be used to update the parameters of the initial model 604 until an acceptable level of accuracy is attained for the refined model.” Applicant specification [0082] discloses that predefined model statistics may include accuracy.)
deploying said deployable model on said user computer, an AI adapter device, and/or a remote processor to permit an inference; wherein said user computer and said remote processor are present at a location that is remote to location of said AI adapter device; ([0089] Haemel recites that the initial model is trained to generate the refined model, and [0090] “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, that will be uploaded to the model registry for deployment or further training. [0021] Haemel recites “deployment system 106” that the “process 100” or “training system 104” may be executed upon “to perform training, deployment, and implementation of machine learning models,” and “the pipeline may be use or call upon services (e.g., inference, visualization, compute, AI, etc.) of the deployment system 106 during execution of the applications.” The deployment system is interpreted as a user computer and/or an AI adapter device. [0031] Haemel recites “the request may then be passed to one or more components of the deployment system 106 (e.g., the cloud) to perform the processing of the data processing pipeline,” in which the deployment system or the cloud is interpreted as a remote processor or system.)
determining whether said inference satisfies one or more predefined inference criteria; ([0039] Haemel recites [0083] Haemel recites the model training process, wherein “the parameters may be updated and re-tuned for a new data set based on loss calculations associated with the accuracy of the output or loss layer(s) at generating predictions on the new, customer dataset 606.” This is interpreted as the loss calculations or output accuracy checking whether the output or inference of the model satisfies a required accuracy or inference criteria.)
deeming said deployable model as final model, if said inference satisfies one or more of said predefined inference criteria, and modifying said deployable model, if said inference does not satisfy one or more of said predefined inference criteria, until said deployable model satisfies one or more of said predefined inference criteria to produce said final model; ([0089] Haemel recites that the initial model is trained to generate the refined model “until an acceptable level of accuracy is attained,” and [0090] Haemel recites “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” “the refined model 612 may be further refined on new datasets any number of times to generate a more universal model.” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, then that 1st refined model being further refined into a final or more universal model. The refined or deployable model being able to be refined “any number of times” is interpreted as being modified until a universal model is generated or the inference criteria is satisfied to produce the final model, as the refining process is understood to be the same as the process described in Haemel [0089].)
implementing said final model on said AI adapter device to draw an inference; and ([0089-0090] Haemel recites a “refined model” or final model that is “deployed within one or more deployment pipelines 210” for “performing one or more processing tasks.” [0021] Haemel recites “deployment system 106” involved in the “inferencing pipeline” and configured to execute services of “inference, visualization, compute, AI, etc.” [0044, 0046] Haemel recites how the “deployment system 106” is applied to “interact with the deployment pipeline(s) 210. [0031] Haemel recites “the request may then be passed to one or more components of the deployment system 106 (e.g., the cloud) to perform the processing of the data processing pipeline,” in which the deployment system or the cloud is interpreted as a remote AI adapter or system.)
Haemel does not teach: creating a mathematical model, using one or more data and/or one or more data attributes and that is describing a phenomenon involving a human, animal, and/or plant presence or behavior at a location of interest;
taking an action, using said AI adapter device, at said location of interest to conserve said human, said animal and/or said plant life.
However, Aldridge teaches in the same field of endeavor: creating a mathematical model, using one or more data and/or one or more data attributes and that is describing a phenomenon involving a human, animal, and/or plant presence or behavior at a location of interest; ([Col. 6 Lines 21-53] Aldridge discusses using a machine learning schema to build a mathematical model that will be used to make predictions such as making inferences from the past movement patterns about how the animal will behave in the future. Aldridge also discusses how the program may identify the influence of the natural environment on animal movement patterns, showing an interconnectedness between wildlife and the location of the observed subject animal.)
taking an action, ([Col. 7 Lines 4-28] Aldridge explains how the program takes the action of generating decoy paths for animals that would make it less likely for a poacher to identify signals corresponding to real animal pathing, helping to conserve animal life.)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Haemel’s teachings by directing the model training and inference generation towards wildlife conversation efforts as taught by Aldridge. One would have been motivated to make this modification in order to improve target environmental or wildlife conservation efforts such as anti-poaching. (Aldridge [Background])
With regard to Claim 17:
Haemel in view of Aldridge teaches: The process of facilitating environmental conservation of claim 16, further comprising conveying said final model to an AI adapter device, if said final model resides on said user computer and/or said remote processor, and wherein said conveying is carried out after said deeming and prior to said implementing. (Examiner understands “said deeming” to refer to the step of “deeming a deployable model as a final model”. [0022] Haemel recites “the training system 104 may be used to provide applications, services, and/or other resources for generating working, deployable machine learning models for the deployment system 106,” in which the deployment system is interpreted as an AI device. [0024-0026] Haemel states that the model may be selected from the model registry, referred to as “the output model 116” or a final model, and then used in the deployment system. This workflow is also interpreted as the deployment system having the selected final model conveyed to it prior to performing the one or more processing tasks, or implementation. [0089] Haemel recites that the initial model is trained to generate the refined model “until an acceptable level of accuracy is attained,” and [0090] Haemel recites “the refined model 612 may be uploaded to the pre-trained models 206 in the model registry 124 to be selected by another facility,” “the refined model 612 may be further refined on new datasets any number of times to generate a more universal model.” This is interpreted as the candidate or initial model being trained into the deployable or 1st refined model, then that 1st refined model being further refined into a final or more universal model. The refined or deployable model being able to be refined “any number of times” is interpreted as being modified until a universal model is generated or the inference criteria is satisfied to produce the final model, as the refining process is understood to be the same as the process described in Haemel [0089].)
With regard to Claim 18:
Haemel in view of Aldridge teaches: The process of facilitating environmental conservation of claim 16, wherein said training, said determining and said deeming are carried out at said user computer or said processor present at said location that is remote to said location of said AI adapter device. ([0025] Haemel recites “the machine learning models in the model registry 124 may have been trained on imaging data from different facilities than the facility 102 (e.g., facilities remotely located)” and that the model is added to the model registry after training, meaning the determining and deeming of the model as deployable or final also occurs at the facility or user computer prior to conveying it to the registry. [0037] Haemel states that the deployment system or AI device may be implemented in a cloud or remote environment.)
With regard to Claim 19:
Haemel in view of Aldridge teaches: The process of facilitating environmental conservation of claim 16, wherein said taking an action includes one action chosen from a group comprising sending a notification to said user computer and/or a third party, setting a trap, sounding an alarm, recording an image or a video, recording a sound, depleting resources consumed by an invasive animal or a plant species, dispersing food, monitoring animal or plant health, and administering medicine or vaccine. ([Col. 7 Lines 4-28, Col. 8 Lines 5-23] Aldridge describes how the anti-poaching program generates decoy paths or mimics signals to mislead the tracking device, this is interpreted as setting a trap. [Col. 6 Lines 8-20, Lines 34-53] Aldridge also describes identifying various factors of the animal’s environment including the presence of potential mates or predators, which is interpreted as monitoring animal health.)
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Haemel in view of Sheffer in further view of Aldridge.
With regard to Claim 4:
Haemel teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference,
Haemel does not explicitly teach: wherein said receiving identification of one or more of said relevant data attributes includes receiving at least one attribute chosen from a group comprising date of creation of said relevant data, time of creation of said relevant data, location coordinates of location from where said relevant data was retrieved, species involved in said relevant data and animal present in said relevant data.
However, Vian teaches in the same field of endeavor: wherein said receiving identification of one or more of said relevant data attributes includes receiving at least one attribute chosen from a group comprising date of creation of said relevant data, time of creation of said relevant data, location coordinates of location from where said relevant data was retrieved, species involved in said relevant data and animal present in said relevant data. ([0082, 0112, 0171] Vian discloses various information that may be collected by the system, including a selected environmental area over a period of time, the presence of a foreign species with respect to trees, and the location of a particular vegetation.)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Haemel’s teachings by ensuring that the data used for model training involves environmental data as taught by Vian. One would have been motivated to make this modification in order to guide the model training towards a more specific environmental goal to accomplish the overall goal of environmental conservation.
With regard to Claim 12:
Haemel in view of Sheffer and Aldridge teaches: The process of claim 11 of allowing a user to automatically create a customized model that permits an inference, further: comprising implementing said final model on said AI adapter device; and ([0089-0090] Haemel recites a “refined model” or final model that is “deployed within one or more deployment pipelines 210” for “performing one or more processing tasks.” [0021] Haemel recites “deployment system 106” involved in the “inferencing pipeline” and configured to execute services of “inference, visualization, compute, AI, etc.” [0044, 0046] Haemel recites how the “deployment system 106” is applied to “interact with the deployment pipeline(s) 210. [0031] Haemel recites “the request may then be passed to one or more components of the deployment system 106 (e.g., the cloud) to perform the processing of the data processing pipeline,” in which the deployment system or the cloud is interpreted as a remote AI adapter or system.)
taking an action, ([Col. 7 Lines 4-28] Aldridge explains how the program takes the action of generating decoy paths for animals that would make it less likely for a poacher to identify signals corresponding to real animal pathing, helping to conserve animal life.)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Haemel in view of Sheffer in further view of Elkabetz et al. (US 2019/0339416 A1), hereinafter “Elkabetz”.
With regard to Claim 5:
Haemel in view of Sheffer teaches: The process of claim 1 of allowing a user to automatically create a customized model that permits an inference,
Haemel in view of Sheffer does not teach: wherein said training includes using situational awareness bias attributes to identify said candidate model, and wherein said situational awareness bias attributes include at least one attribute chosen from a group comprises geographical data of said relevant data, temporal data of said relevant data, weather conditions during retrieval of said relevant data, and previous inferences drawn from said relevant data.
However, Elkabetz teaches in the same field of endeavor: wherein said training includes using situational awareness bias attributes to identify said candidate model, and wherein said situational awareness bias attributes include at least one attribute chosen from a group comprises geographical data of said relevant data, temporal data of said relevant data, weather conditions during retrieval of said relevant data, and previous inferences drawn from said relevant data. ([0070] Elkabetz recites “geo-information” as a source of data to support inference generation. [0109-0114] Elkabetz describes the fog time series analysis program that analyzes time series information or relevant temporal data. [0110] Elkabetz explains that weather data may be analyzed to generate a fog inference. [0114] Elkabetz explains that the ML model may examine previous inferences to help predict its next fog inference.)
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Haemel in view of Sheffer in further view of Gharabegian (US 2019/0014643 A1).
With regard to Claim 7:
Haemel in view of Sheffer and Gharabegian teaches: The process of claim 6 of allowing a user to automatically create a customized model that permits an inference, further comprising:
Haemel in view of Sheffer does not teach: conveying operational instructions pertinent to one or more of said relevant data attributes to one or more controllers that control operation of said visual and/or audio sensors; and
changing, based upon said operational instructions, operating conditions of said visual and/or audio sensors for collecting said relevant data to produce at least a portion of said relevant data.
However, Gharabegian teaches in the same field of endeavor: conveying operational instructions pertinent to one or more of said relevant data attributes to one or more controllers that control operation of said visual and/or audio sensors; and ([0068] Gharabegian recites that “an AI device and lighting system 570” may receive a relevant data such as one or more audio files containing a voice command and convey that command to a “controller/processor 571”, and the controller will then generate an instruction to cause “devices 572 to perform an action requested” such as “turn on camera and/or sensors”.)
changing, based upon said operational instructions, operating conditions of said visual and/or audio sensors for collecting said relevant data to produce at least a portion of said relevant data. ([0091] Gharabegian describes how based on received solar power measurements from a sensor reading, a processor may communicate instructions to a controller to move a base assembly towards a direction where solar panels may capture more solar power.)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Haemel in view of Sheffer’s teachings by instructing the controller to target/direct sensor data as taught by Gharabegian. One would have been motivated to make this modification in order to improve the quality of the data that is collected and used in model training, allowing it to be more relevant to the purpose of the machine learning model.
With regard to Claim 8:
Haemel in view of Sheffer and Gharabegian teaches: The process of claim 7 of allowing a user to automatically create a customized model that permits an inference, wherein said changing includes producing a portion, and not entire, of said relevant data. ([0091] Gharabegian describes how the assembly may move in order to capture additional different data, based on the received sensor data. This is interpreted as the apparatus producing a portion of the relevant data, before adjusting itself to produce another portion of relevant data.)
Claims 23 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Haemel in view of Gharabegian.
With regard to Claim 23:
Haemel teaches: The audio/visual data collection device of claim 20,
Haemel does not explicitly teach: wherein said AI processor, said communication component, and said power source are on a single printed circuit board.
However, Gharabegian teaches in the same field of endeavor: wherein said AI processor, said communication component, and said power source are on a single printed circuit board. ([0039] Gharabegian recites that “AI device housing 108 may comprise one or more processors/controllers 127,” and “one or more processors 127… may be integrated into a single-board computing device 120”)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Haemel’s teachings by providing a single printed circuit board to connect the components of the invention as taught by Gharabegian. One would have been motivated to make this modification in order to efficiently contain the components of the apparatus within one enclosure for ease of use and/or ease of access.
With regard to Claim 28:
Haemel in view of Gharabegian teaches: The audio/visual data collection device of claim 27 and a housing ([Fig. 2 AI System 224] Haemel illustrates a housing for the AI system. [0039] Gharabegian recites “AI device housing 108.”)
wherein said housing is designed to house therein: an audio and/or visual data sensor for collecting audio and/or visual data; (Examiner understands “said housing” to refer to a housing of the AI adapter device that encapsulates the sensors, controller, and AI processor. [0039] Gharabegian recites that the “AI device housing 108” may comprise “one or more microphones (or audio receiving devices) 129” which is interpreted as an audio data sensor.)
an audio and/or visual data controllers for controlling operation of said audio and/or said visual data sensor; ([0068] Gharabegian recites “controller/processor 571” that may generate a command or instruction to “devices 572” to perform an action such as “turn on camera and/or sensors”.)
an AI processor that provides instructions to said audio and/or said visual data controllers; and ([0068] Gharabegian recites that “an AI device and lighting system 570” may receive and convey a command to a “controller/processor 571”, and the controller will then generate an instruction to cause “devices 572 to perform an action requested” such as “turn on camera and/or sensors”.)
wherein said housing has connecting features that allow connection between said printed circuit board and said audio and/or said visual data controllers, and (Examiner understands “said printed circuit board” to refer to a single printed circuit board connected to the processor and controllers. [0039] Gharabegian recites that “AI device housing 108 may comprise one or more processors/controllers 127,” and “one or more processors 127… may be integrated into a single-board computing device 120”)
wherein said printed circuit board includes said AI adapter device memory. ([0039] Gharabegian recites “one or more memories 128… may be integrated into a single-board computing device 120.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sarkar (US 2021/0224684 A1) discusses scoring model inferences against a model validity threshold.
Sigl (“Don’t Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models”) discusses reusing prebuilt models through selecting them through a repository and additionally training them with new pertinent data.
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/BRIAN D. BUI/Examiner, Art Unit 2127
/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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