DETAILED ACTION
Remarks
The present application was filed 22 September 2024, is a 371 of PCT/JP2023/009424 filed on 10 March 2023 and claims priority to JP2022-056221 filed in Japan on 30 March 2022.
A preliminary amendment was filed on 22 September 2024 adding a new paragraph [0000] the specification.
Claims 1-12 are pending.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 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.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
The “…information acquiring section that acquires information…” in claim 1;
The “…model selecting section that selects…” in claim 1;
The …transmission processing section that performs transmission control…” in claim 1;
The “…information acquiring section acquires a result of the inference…” in claim 2;
The “…handling processing section that performs a process according to the purpose information…” in claim 2;
The “…transmission section that transmits…” in claim 9;
The “…reception processing section that receives an artificial intelligence model…” in claim 9;
The “…inference processing section that performs an inference process…” in claim 9;
The “…transmission processing section transmits a result of inference…” in claim 9;
The “…transmission processing section transmits the information...” in claim 10;
The “…information acquiring section that acquires information…” in claim 12;
The “…model selecting section that selects…” in claim 12;
The …transmission processing section that performs transmission control for transmitting the selected artificial intelligence model…” in claim 12;
The …transmission processing section that performs transmission control for transmitting the information acquired from the sensor…” in claim 12;
The “…reception processing section that receives an artificial intelligence model…” in claim 12;
The “…inference processing section that performs an inference process…” in claim 12;
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Selection and Deployment of Artificial Intelligence Models to Edge-Side Devices on the Basis of Purpose Information and Sensor Information
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: S42 (Fig. 4); PS21 (Fig. 19).
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) 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. 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.
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.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 1-2, 9-10 and 12, various limitations of these claims noted above invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph as noted. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed functions of each limitation and to clearly link the structure, material, or acts to the claimed functions. For computer-implemented means plus function limitations, note that the disclosed structure must include an algorithm for performing the function claimed. See MPEP § 2181(II)(B). And the specification provides no algorithm sufficient for performing any of the claimed functions here. It only repeats the language of the claims.
Since the specification lacks sufficient corresponding structure, the claim is indefinite and an equivalent is any element that performs the specified function. See M.P.E.P. §§ 2181(II)(B) and 2185.
As to claims 3-8 and 11 the claims are dependent on claim 1 or 9 but do not cure the deficiencies of that claims.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claims 1-2, 9-10 and 12, the claims include means-plus function limitations lacking sufficient corresponding structure as noted above. Such limitations also lack written description. See M.P.E.P. § 2163.03(VI).
As to claims 3-8 and 11 the claims are dependent on claim 1 or 9 and lack written description for the reasons set forth above with respect to claim 1 and 9.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto et al. (US 2019/0147360) (art made of record – hereinafter Matsumoto) in view of Tomioka et al. (US 2019/0130216) (art made of record – hereinafter Tomioka) in view of Sobot et al. (US 2022/0343004) (art made of record – hereinafter Sobot).
As to claim 1, Matsumoto discloses an information processing apparatus comprising:
a model selecting section that selects an artificial intelligence model to be deployed on the edge-side information processing apparatus, on a basis of purpose information set by a user; (e.g., Matsumoto, par. [0058]: the user operates input unit 3 to input needs information. The needs information includes request model information; par. [0059]: the request model information includes the function of the learned model; par. [0060]: the function of the learned model is the use purpose or use application of the learned model [purpose information]; par. [0065]: server device 2 selects the learned model from the plurality of learned models; par. [0013]: the learned model can be selected from a plurality of learned models based on the needs information; par. [0069]: server device 2 transmits the learned model to user side device 3; par. [0053]: user side device 3 is used for performing image analysis processing using the learned model from server device. Provision of the model from server device to user side device is performed by user side device 3 transmitting a request to server device) and
a transmission processing section that performs transmission control for transmitting the selected artificial intelligence model to the edge-side information processing apparatus, (see immediately above).
Matsumoto does not explicitly disclose an information acquiring section that acquires information from a sensor of an edge-side information processing apparatus that performs an inference process using an artificial intelligence model; a model selecting section that selects an artificial intelligence model on a basis of information acquired from the sensor; or transmitting the artificial intelligence model on a basis of a confirmation result of purchase information regarding the artificial intelligence model in an account associated with the user.
However, in an analogous art, Tomioka discloses:
an information acquiring section that acquires information from a sensor of an edge-side information processing apparatus that performs an inference process using an artificial intelligence model; (e.g., Tomioka, par. [0022]: mobile terminal [edge-side information processing apparatus] is provided with a camera [sensor], which serves as the image capturing apparatus; par. [0033]: the geometric information estimation unit 140 inputs, to the learning model, the input image and estimates geometric information [performs an inference process]; par. [0074]: mobile terminal [edge device] transfers an input image to the cloud server)
a model selecting section that selects an artificial intelligence model on a basis of information acquired from the sensor; (e.g., Tomioka, par. [0074]: the learning model selection unit 120 on the cloud service calculates evaluation values of the learning models and selects a learning model based on the evaluation result; abstract: an evaluation value indicates suitability of the learning model to a scene of the input image).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and model selection section of Matsumoto to include an information acquiring section that acquires information from a sensor of an edge-side information processing apparatus that performs an inference process using an artificial intelligence model and selecting an artificial intelligence model on a basis of information acquired from the sensor, as taught by Tomioka, as Tomioka would provide the advantage of a means for selecting a model that can more accurately perform the desired function. (See Tomioka, par. [0058]).
Further, in an analogous art, Sobot discloses:
transmitting the artificial intelligence model on a basis of a confirmation result of purchase information regarding the artificial intelligence model in an account associated with the user (e.g., Sobot, par. [0068]: fee-based subscription can provide features that the free subscription does not provide; Fig. 3 and associated text, par. [0108]: at 302 a request is received. The request is associated with a machine learning model used to enable the feature. At 304, an initial decision is made as to whether the feature 110 is accessible. The decision can be made based on the user-specific information included in the request. As one example, the user identifier can be tied to a user account, for example, and a subscription type associated with the user account can be used to initially determine whether the user is entitled to access the feature 110 based on a subscription type of the user account at least matching or exceeding a subscription type needed to access the feature 110; par. [0112]: if the feature 110 is determined not to be accessible based on the information included in the request received at operation 302, re-creation and sending of the obfuscation key will be refused [and the model, which will only be sent at 312 if the feature is accessible, see 304 and 312 of figure]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the selected artificial intelligence model transmission of Matsumoto to include transmitting the artificial intelligence model on a basis of a confirmation result of purchase information regarding the artificial intelligence model in an account associated with the user, as taught by Sobot, as Sobot would provide the advantage of a means of ensuring the model is only accessed by paying users. (See Sobot, par. [0108], [0068]).
As to claim 3, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 1 (see rejection of claim 1 above), Matsumoto further discloses:
wherein the purpose information is information regarding a purpose of use of a result of inference of the inference process (e.g., Matsumoto, par. [0050]: the function of the learned model is the use purpose or use application of the learned model. For example, in a case where the learned model is a learned model that performs some estimation on a person from a captured image including the person, examples of functions of the learned model include a face detection function, a human body detection function, a motion detection function, a posture detection function, a person attribute estimation function, a person behavior prediction function, and the like).
As to claim 4, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 3 (see rejection of claim 3 above), Matsumoto further discloses:
wherein information related to the sensor is information regarding a sensor type (e.g., Matsumoto, par. [0051]: The generation environment information includes information on at least one of an acquiring condition of learning data and an installation environment of a device used for acquiring the learning data. For example, in a case where the learning data is a captured image and the device used for acquiring the learning data is a camera, examples of acquiring conditions of learning data include imaging time (for example, day, night, or the like).
As to claim 5, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 3 (see rejection of claim 3 above), Matsumoto further discloses:
wherein information related to the sensor is positional information as sensing information (e.g., Matsumoto, par. [0061]: information on an acquisition environment includes information on at least one acquiring condition and an installation environment. Acquiring condition include various imaging parameters “(for example, installation height, imaging angle, focal distance…or the like)”. Examples of installation environment of the device include a place where the camera is installed and the environment around the camera).
As to claim 6, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 5 (see rejection of claim 1 above), Matsumoto further disloses:
wherein the purpose of use is a purpose to acquire information specific to each area (e.g., Matsumoto, par. [0050]: examples of functions of the learned model include a face detection function, a posture detection function [a face or human body is an area]).
As to claim 12, Matsumoto discloses an information processing system comprising:
a server apparatus; (e.g., Matsumoto, par. [0065]: server device 2) and
an edge-side information processing apparatus, (e.g., Matsumoto, par. [0045]: user side deice 3) wherein the server apparatus has
a model selecting section that selects an artificial intelligence model to be deployed on the edge-side information processing apparatus, on a basis of purpose information set by a user and the information acquired from the sensor, (e.g., Matsumoto, par. [0058]: the user operates input unit 3 to input needs information. The needs information includes request model information; par. [0059]: the request model information includes the function of the learned model; par. [0060]: the function of the learned model is the use purpose or use application of the learned model [purpose information]; par. [0065]: server device 2 selects the learned model from the plurality of learned models; par. [0013]: the learned model can be selected from a plurality of learned models based on the needs information; par. [0069]: server device 2 transmits the learned model to user side device 3; par. [0053]: user side device 3 is used for performing image analysis processing using the learned model from server device. Provision of the model from server device to user side device is performed by user side device 3 transmitting a request to server device) and
a transmission processing section that performs transmission control for transmitting the selected artificial intelligence model to the edge-side information processing apparatus, (see immediately above)
the edge-side information processing apparatus has
a reception processing section that performs reception control for receiving the selected artificial intelligence model, (e.g., Matsumoto, par. [0069]: server device 2 transmits the learned model to user side device 3) and
an inference processing section that performs an inference process using the received artificial intelligence model (e.g., Matsumoto, par. [0053]: user side device 3 is used for performing image analysis processing using the learned model from server device; par. [0060]: a learned model performs some estimation [inference] on a person from a captured image).
Matsumoto does not explicitly disclose wherein the server apparatus has an information acquiring section that acquires information from a sensor of the edge-side information processing apparatus; a model selecting section that selects an artificial intelligence model on a basis of the information acquired from the sensor; transmitting the selected artificial intelligence model on a basis of a confirmation result of purchase information regarding the artificial intelligence model in an account associated with the user; the edge-side information processing apparatus has a transmission processing section that performs transmission control for transmitting the information acquired from the sensor to the server apparatus.
However, in an analogous art, Tomioka discloses:
the server apparatus has
an information acquiring section that acquires information from a sensor of the edge-side information processing apparatus (e.g., Tomioka, par. [0022]: mobile terminal [edge-side information processing apparatus] is provided with a camera [sensor], which serves as the image capturing apparatus; par. [0033]: the geometric information estimation unit 140 inputs, to the learning model, the input image and estimates geometric information [performs an inference process]; par. [0074]: mobile terminal [edge device] transfers an input image to the cloud server)
a model selecting section that selects an artificial intelligence model on a basis of the information acquired from the sensor (e.g., Tomioka, par. [0074]: the learning model selection unit 120 on the cloud service calculates evaluation values of the learning models and selects a learning model based on the evaluation result; abstract: an evaluation value indicates suitability of the learning model to a scene of the input image)
the edge-side information processing apparatus has
a transmission processing section that performs transmission control for transmitting the information acquired from the sensor to the server apparatus, (e.g., Tomioka, par. [0022]: mobile terminal [edge-side information processing apparatus] is provided with a camera [sensor], which serves as the image capturing apparatus; par. [0033]: the geometric information estimation unit 140 inputs, to the learning model, the input image and estimates geometric information [performs an inference process]; par. [0074]: mobile terminal [edge device] transfers an input image to the cloud server).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and model selection section of Matsumoto to include an information acquiring section that acquires information from a sensor of an edge-side information processing apparatus that performs an inference process using an artificial intelligence model and selecting an artificial intelligence model on a basis of information acquired from the sensor, as taught by Tomioka, as Tomioka would provide the advantage of a means for selecting a model that can more accurately perform the desired function. (See Tomioka, par. [0058]).
Further, in an analogous art, Sobot discloses:
transmitting the selected artificial intelligence model on a basis of a confirmation result of purchase information regarding the artificial intelligence model in an account associated with the user (e.g., Sobot, par. [0068]: fee-based subscription can provide features that the free subscription does not provide; Fig. 3 and associated text, par. [0108]: at 302 a request is received. The request is associated with a machine learning model used to enable the feature. At 304, an initial decision is made as to whether the feature 110 is accessible. The decision can be made based on the user-specific information included in the request. As one example, the user identifier can be tied to a user account, for example, and a subscription type associated with the user account can be used to initially determine whether the user is entitled to access the feature 110 based on a subscription type of the user account at least matching or exceeding a subscription type needed to access the feature 110; par. [0112]: if the feature 110 is determined not to be accessible based on the information included in the request received at operation 302, re-creation and sending of the obfuscation key will be refused [and the model, which will only be sent at 312 if the feature is accessible, see 304 and 312 of figure]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the selected artificial intelligence model transmission of Matsumoto to include transmitting the artificial intelligence model on a basis of a confirmation result of purchase information regarding the artificial intelligence model in an account associated with the user, as taught by Sobot, as Sobot would provide the advantage of a means of ensuring the model is only accessed by paying users. (See Sobot, par. [0108], [0068]).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (US 2019/0147360) in view of Tomioka (US 2019/0130216) in view of Sobot (US 2022/0343004) in further view of Dehkordi et al. (US 2022/0414432) (art made of record – hereinafter Dehkordi).
As to claim 2, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 1 (see rejection of claim 1 above), but does not explicitly disclose wherein the information acquiring section acquires a result of inference using the selected artificial intelligence model from the edge-side information processing apparatus, and the information processing apparatus includes a handling processing section that performs a process according to the purpose information by using the result of inference.
However, in an analogous art, Dehkordi discloses wherein
wherein the information acquiring section acquires a result of inference using the artificial intelligence model from the edge-side information processing apparatus, (e.g., Dehkordi, par. [0007]: the outputs [results of inference] generated by the smaller deep learning models running on the edge device are sent to [acquired by] the cloud) and
the information processing apparatus includes a handling processing section that performs a process according to the purpose information by using the result of inference (e.g., Dehkordi, par. [0007]: in edge-cloud collaborations, a software program that implements a deep learning model which performs a particular inference task can be broken up into multiple programs that perform the particular inference task [purpose information]. Some of these smaller programs can be run on edge devices and the rest run in the cloud. The outputs generated by the smaller deep learning models running on the edge device are sent to the cloud for further processing by the rest of the models running in the cloud).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the server of selection of models for performing inference on edge devices taught by Matsumoto such that the server includes a section acquires a result of inference using the artificial intelligence model from the edge-side information processing apparatus and a section that performs a process according to the purpose information by using the result of inference as taught by Dehkordi, as Dehkordi would provide the advantages of a means of performing edge cloud collaboration and a means of reducing the volume of data transmitted to the server. (See Dehkordi, pars. [0006], [0005]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (US 2019/0147360) in view of Tomioka (US 2019/0130216) in view of Sobot (US 2022/0343004) in further view of Gawande et al. (US 2020/00148213) (art made of record – hereinafter Gawande).
As to claim 7, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 3 (see rejection of claim3 above), but does not explicitly disclose wherein the purpose of use is a purpose to obtain information regarding a road condition.
However, in an analogous art, Gawande discloses:
wherein the purpose of use is a purpose to obtain information regarding a road condition (e.g., Gawande, par. [0018]: the present disclosure uses ML models to detect the road friction or road surface condition beneath a vehicle and predict the road friction or road surface condition ahead of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the purpose of use of models taught by Matsumoto such that the purpose of use is a purpose to obtain information regarding a road condition as taught by Gawande, as Gawande would provide the advantage of a means of improving vehicle warning and response systems. (See Gawande, par. [0006]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (US 2019/0147360) in view of Tomioka (US 2019/0130216) in view of Sobot (US 2022/0343004) in further view of Zarakas et al. (US 2022/0138785) (art made of record – hereinafter Zarakas).
As to claim 8, Matsumoto/Tomioka/Sobot discloses the information processing apparatus according to claim 3 (see rejection of claim 3 above), but does not explicitly disclose wherein the purpose of use is a purpose to obtain price information regarding a fuel to be used for a vehicle.
However, in an analogous art, Zarakas discloses:
wherein the purpose of use is a purpose to obtain price information regarding a fuel to be used for a vehicle (Zarakas, par. [0035]: the machine learning model may determine the fuel price for a fuel station by predicting a fuel price for the fuel station).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the purpose of use of models taught by Matsumoto such that the purpose of use is a purpose to obtain price information regarding a fuel to be used for a vehicle as taught by Zarakas, as Zarakas would provide the advantages of a means to more correctly identify fuel prices of fuel stations and means to reduce costs of higher fuel prices. (See Zarakas, pars. [0011] and [0060]).
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (US 2019/0147360) in view of Dehkordi (US 2022/0414432).
As to claim 9, Matsumoto discloses an information processing apparatus comprising:
a transmission processing section that transmits information related to a sensor to a server apparatus; (e.g., Matsumoto, par. [0063]: the user side resource information, the use environment information and the user side resource information are transmitted to the server device 2 via a network; par. [0061]: is information on an acquisition environment of the user side data “(sensing data)” used. For example, in a case where the device used for acquiring the user side data is a camera, examples of acquiring conditions of user side data include various imaging parameters “(for example, installation height, imaging angle, focal distance, zoom magnification, resolution, or the like)” of the camera, and the like).
a reception processing section that receives an artificial intelligence model selected on a basis of purpose information and the transmitted information related to the sensor; e.g., Matsumoto, par. [0059]: the request model information includes the function of the learned model; par. [0060]: the function of the learned model is the use purpose or use application of the learned model [purpose information]; par. [0065]: server device 2 selects the learned model from the plurality of learned models; par. [0013]: the learned model can be selected from a plurality of learned models based on the needs information; par. [0069]: server device 2 transmits the learned model to user side device 3) and
an inference processing section that performs an inference process using the received artificial intelligence model, (e.g., Matsumoto, par. [0053]: user side device 3 is used for performing image analysis processing using the learned model from server device; par. [0060]: a learned model performs some estimation [inference] on a person from a captured image).
Matsumoto does not explicitly disclose wherein the transmission processing section transmits a result of inference of the inference process to the server apparatus.
However, in an analogous art, Dehkordi discloses:
wherein the transmission processing section transmits a result of inference of the inference process to the server apparatus (e.g., Dehkordi, par. [0007]: the outputs [results of inference] generated by the smaller deep learning models running on the edge device are sent to the cloud [server apparatus])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the inference process and transmission processing section of Matsumoto such that the transmission processing section transmits a result of inference of the inference process to the server apparatus, as taught by Dehkordi as Dehkordi would provide the advantages of a means of performing edge cloud collaboration and a means of reducing the volume of data transmitted to the server. (See Dehkordi, pars. [0006], [0005]).
As to claim 10, Matsumoto/Dehkordi discloses the information processing apparatus according to claim 9 (see rejection of claim 9 above), Mastumoto further discloses:
wherein the transmission processing section transmits the information related to the sensor at a time of activation (e.g., Matsumoto, par. [0063]: the request model information, the use environment information, and the user side resource information input to the user side device 3 are transmitted to server device [so at least the user side device has been activated]).
As to claim 11, Matsumoto/Dehkordi discloses the information processing apparatus according to claim 9 (see rejection of claim 9 above), Matsumoto further discloses:
wherein the information related to the sensor is positional information as sensing information (e.g., Matsumoto, par. [0061]: information on an acquisition environment includes information on at least one acquiring condition and an installation environment. Acquiring condition include various imaging parameters “(for example, installation height, imaging angle, focal distance…or the like)”. Examples of installation environment of the device include a place where the camera is installed and the environment around the camera).
Conclusion
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/TODD AGUILERA/Primary Examiner, Art Unit 2192
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