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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 14th, 2024 was reviewed and the listed references were noted.
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: 101 and It1 in Figure 3. 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.
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 disclosure is objected to because of the following informalities:
For Page 3, line 16, “tasks “1” to “16”” should read “tasks 1 to 16”.
Appropriate correction is required.
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: “A processing system, configured to...” in claims 1-8
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.
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.
Claim 10 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.
Claim 10 recites the limitation "the processing device" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-6, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Masao et al. (JP 2022055057 A) in view of Zeng et al. (Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation).
Regarding Claim 1, Masao discloses “A processing system, configured to:” (Masao, Abstract, discloses: “In the work analysis system, a work analysis apparatus 10 that is operated by a work analysis program includes: a skeleton extraction unit 11 that acquires skeleton data 19 including feature point data indicating joint positions of a person in image data 18 by performing image recognition of the image data 18 as input; a mark recognition unit 15 that recognizes specific marks in the image data 18; and an analysis unit 13 that analyzes a work done by a person based on the marks recognized by the mark recognition unit 15 and specific parts of the skeleton data 19 acquired by the skeleton extraction unit 11”); and “generate first graph data based on a pose of a worker, the pose being estimated based on a first image of the worker, the first graph data including a plurality of first nodes corresponding respectively to a plurality of joints of the worker” (Masao, discloses the following process in Paragraphs [0021]-[0024] and Figures 2A-2C (See below):
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)in an analogous field of endeavor, Zeng discloses the following in
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Section 2.1 (Page 2, Col, 2) and Figure 2:
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Here, it can be seen that in addition to the plurality of nodes that we see in both references, the plurality of edges are clearly defined as bone connections, which are synonymous to the skeletal parts of a worker. Zeng also discloses the following in Figure 1 and Page 1, Column 2, and Page 2, Column 2:
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Accordingly, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for generating first graph data seen in Masao with the GNN-based techniques and acquisition of skeletal data with a plurality of nodes and edges to assess pose seen in Zeng to achieve a complete processing system for analyzing the pose of a worker. By combining both of these references, one of ordinary skill in the art can achieve the determination as to whether a worker is fulfilling a specific task through the use of GNN with increased accuracy throughout the pose estimation process. Therefore, it would have been obvious for one of ordinary skill in the art to combine the Masao and Zeng references to achieve the invention described in Claim 1.
Regarding Claim 5, the combination of Masao and Zeng discloses “The system according to claim 1, further configured to:” (Masao, Abstract, Paragraphs [0021]-[0024] and Figures 2A-2C; Zeng, Section 2.1 (Page 2, Col, 2), Page 1, Column 2 and Page 2, Column 2, and Figures 1 and 2, please refer to the above-described analysis for Claim 1), “generate the first graph data based on a state of an article in addition to the pose, the article being visible in the first image” (Masao, Paragraphs [0032]-[0034] and Figures
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5-7, discloses the following:
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); “the state of the article being estimated based on the first image” (Masao, Paragraphs [0052]-[0054], discloses the following:
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), and “the first graph data including: the plurality of first nodes; the plurality of first edges” (Zeng, Section 2.1 (Page 2, Col, 2) and Figure 2, discloses the following:
); and “a plurality of second nodes corresponding respectively to a plurality of the states that the article may be in.” (Masao, Paragraph [0020], discloses: The model generation unit 12 generates the model data 22 by inputting the image data 18a
(image data 18) for learning and the skeleton data 19a (skeleton data 19) for learning, and stores the model data 22 in the non-volatile memory. The model data 22 includes definition data explicitly defined by the user and learned data which is a learning result using the label data input by the user. The model generation unit 12 basically needs to create the model data 22 once for the work to be analyzed, but in order to improve the accuracy, the model data 22 already created is updated (improved). You may. The mark recognition unit 15 recognizes a specific mark contained in the image data 18. The marks recognized by the mark recognition unit 15 include a mark used for setting an area in image data (hereinafter referred to as "first mark") and a mark used for determining the direction of an object (hereinafter referred to as "second mark"). ), A mark used to identify an individual worker (hereinafter referred to as "third mark"), and the like are included. Details of these marks will be described
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later.”; Paragraph [0032]-[0034] and Figures 5-7 (see above) discloses:
). Here, the marks can be considered a plurality of second nodes because as the object/article is being moved throughout the video as each image is captured, the marks allow the system to determine whether the article changed states. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the analysis techniques regarding the state of an article found in the combination of Masao and Zeng to achieve the invention of Claim 5.
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Regarding Claim 6, the combination of Masao and Zeng discloses “The system according to claim 1, further configured to:” (Masao, Abstract, Paragraphs [0021]-[0024] and Figures 2A-2C; Zeng, Section 2.1 (Page 2, Col, 2), Page 1, Column 2 and Page 2, Column 2, and Figures 1 and 2, please refer to the above-described analysis for Claim 1), “generate the first graph data based on a work location on an article in addition to the pose, the article being visible in the first image,” (Masao, Paragraphs [0032]-[0034] and Figures 5-7, discloses the following:
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); “the work location on the article being estimated based on the first image” (Masao, Paragraphs [0052]-[0054], discloses the following:
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); and “the first graph data including: the plurality of first nodes; the plurality of first edges” (Zeng, Section 2.1 (Page 2, Col, 2) and Figure 2, discloses the following:
); and “a plurality of third nodes corresponding respectively to a plurality of locations of the article.” (Masao, Paragraph [0020], discloses: The model generation unit 12 generates the model data 22 by inputting the image data 18a (image data 18) for learning and the skeleton data 19a (skeleton data 19) for learning, and stores the model data 22 in the non-volatile memory. The model data 22 includes definition data explicitly defined by the user and learned data which is a learning result using the label data input by the user. The model generation unit 12 basically needs to create the model data 22 once for the work to be analyzed, but in order to improve the accuracy, the model data 22 already created is updated (improved). You may. The mark recognition unit 15 recognizes a specific mark contained in the image data 18. The marks recognized by the mark recognition unit 15 include a mark used for setting an area in image data (hereinafter referred to as "first mark") and a mark used for determining the direction of an object (hereinafter referred to as "second mark"). ), A mark used to identify an individual worker (hereinafter referred to as "third mark"), and the like are included. Details of these marks will be described
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later.”; Paragraph [0032] - [0034] and Figures 5-7 (see above) discloses:
). Here, the marks can be considered a plurality of third nodes because in Figures 5-7 the first and second marks allow the work analysis system to determine where the locations of the article are to assess the work that the worker is doing within each image captured as the camera films what is being done in real-time. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the analysis techniques regarding the work locations of an article found in the combination of Masao and Zeng to achieve the invention of Claim 6.
Claim 9 recites a method with steps corresponding to the elements of the system recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Masao and Zeng references, presented in rejection of Claim 1, apply to this claim.
Claim 10 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 9. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Masao and Zeng references, presented in rejection of Claim 9, apply to this claim. Finally, the combination of Masao and Zeng references discloses a computer readable storage medium (for example, see Masao, Paragraph [0062]).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Masao in view of Zeng, and further in view of Yasuo Namioka (US 2019/0065949).
Regarding Claim 2, the combination of Masao and Zeng discloses “The system according to claim 1, further configured to:” (Masao, Abstract, Paragraphs [0021]-[0024] and Figures 2A-2C; Zeng, Section 2.1 (Page 2, Col 2), Page 1, Column 2 and Page 2, Column 2, and Figures 1 and 2, please refer to the above-described analysis for Claim 1); (Zeng, Section 2.1 (Page 2, Col 2) and Figure 2, disclose the
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following:
); Namioka, Paragraph [0019]). Namioka also discloses in the same paragraph that “The trainer trains a recurrent neural network including a first output layer. The first output layer includes a first neuron and a second neuron. The trainer trains the recurrent neural network by setting a first value as teacher data in the first neuron and by inputting the first data to the recurrent neural network. The first value corresponds to the action of the first body part of the first proficiency. The trainer trains the recurrent neural network by setting a second value as teacher data in the second neuron and by inputting the second data to the recurrent neural network. The second value corresponds to the action of the first body part of the second proficiency. The detector inputs the third data to the trained recurrent neural network and detects a response of the first neuron or the second neuron.” (Namioka, Paragraph [0019]). Therefore, it would have been obvious for one of ordinary skill of the art before the effective filing date of the claimed invention to combine the complete processing system discloses in the combination of Masao and Zeng with acquisition of the second graph data and insertion of that data into the neural network to achieve a result output that is seen in Namioka to achieve better task estimation within the system. By using the acquisition and insertion of the second graph data seen in Namioka, one of ordinary skill in the art can further increase the accuracy by understanding changes between the different sets of data to have better stability within the system for estimating a task. Therefore, it would be obvious for one of ordinary skill in the art to combine the processing system seen in the combination of Masao and Zeng with the second graph data techniques seen in Namioka to achieve the invention of Claim 2.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Masao in view of Zeng, and further in view of Namioka and Zhang et al. (Non-local Graph Convolutional Network for Joint Activity Recognition and Motion Prediction).
Regarding Claim 3, the combination of Masao, Zeng, and Namioka discloses “The system according to claim 2, wherein the neural network includes the GNN” (Masao, Abstract, Paragraphs [0021]-[0024] and Figures 2A-2C; Zeng, Section 2.1 (Page 2, Col 2), Page 1, Col 2 and Page 2, Col 2, and Figures 1 and 2)(Zhang, Page 1, Col 2), where they describe the following in Page 2, Col 1 and 2 and Figure 1 (See below):
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As shown in the above passage and Figure 1, it is clearly seen that the GCN (Which is another form of a GNN) is being fed as an input into a LSTM network for further processing to estimate the pose of a human. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for task estimation disclosed in the combination of Masao, Zhang, and Namioka with the technique of feeding a GNN into a LSTM network seen in Zhang to achieve the invention of Claim 3.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Masao in view of Zeng, and further in view of Namioka and Hu et al. (CN 116206328 A).
Regarding Claim 4, the combination of Masao and Zeng disclose “The system according to claim 1, further configured to:” (Masao, Abstract, Paragraphs [0021]-[0024] and Figures 2A-2C; Zeng, Section 2.1 (Page 2, Col 2), Page 1, Column 2 and Page 2, Column 2, and Figures 1 and 2, please refer to the above-described analysis for Claim 1)
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of edges (Zeng, Section 2.1 and Figure 2 discloses the following:
)of the first body part of the first proficiency. The trainer trains the recurrent neural network by setting a second value as teacher data in the second neuron and by inputting the second data to the recurrent neural network. The second value corresponds to the action of the first body part of the second proficiency. The detector inputs the third data to the trained recurrent neural network and detects a response of the first neuron or the second neuron” (Namioka, Paragraph [0019]). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the processing system disclosed in the combination of Masao and Zeng with the technique of finding the second graph data as seen in Namioka to achieve the above-described limitations of Claim 4.
The combination of Masao, Zeng, and Namioka does not explicitly disclose “estimate the task by inputting graph data to the neural network and by using the result output from the neural network, the plurality of first nodes of the first graph data and the plurality of nodes of the second graph data being respectively connected by a plurality of edges in the graph data”. However, in an analogous field of endeavor, Hu discloses the following in Paragraphs [0056], [0057], [0060] and Figure 1 (see below):
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Accordingly, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system that processes both first and second graph data found in the combination of Masao, Zeng, and Namioka with the graph data including the edge connections between nodes of the first and second graph data being inputted into a neural network seen in Hu to achieve a complete method of accurately estimating a task. These edge connections of the nodes allow the neural network to better associate different sets of similar data together to see what the worker is performing throughout the course of a said period of time. Thus, it would be obvious for one of ordinary skill in the art to combine the task estimation system found in the combination of Masao, Zeng, and Namioka with the acquisition of graph data found in Hu to achieve a complete task estimation model for the processing system described in Claim 4.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Masao in view of Zeng, and further in view of Yasuo Namioka (US 2021/0081716, referred to as Namioka 2).
Regarding Claim 7, the combination of Masao and Zeng discloses “The system according to claim 1, wherein” (Masao, Abstract, Paragraphs [0021]-[0024] and Figures 2A-2C; Zeng, Section 2.1 (Page 2, Col 2), Page 1, Column 2 and Page 2, Column 2, and Figures 1 and 2, please refer to the above-described analysis for Claim 1) (Namioka 2, Paragraph [0153], Figures 22A and 22B (see below)).
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Accordingly, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the processing system disclosed in the combination of Masao and Zeng with the technique of finding the coordinates for the first graph data seen in Namioka 2 to achieve the invention of Claim 7.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Masao in view of Zeng, and further in view of Namioka et al. (US 2017/0132780, Referred to as Namioka 3).
Regarding Claim 8, the combination of Masao and Zeng discloses “The system according to claim 1, further configured to:” (Namioka 3, Paragraph [0023]). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the processing system disclosed in the combination of Masao and Zeng with the analysis apparatus featuring a display found in Namioka 3 to assess the efficiency of worker activities throughout the work day. By using a display device as seen in Namioka 3 with the processing system disclosed in the combination of Masao and Zeng, one of ordinary skill in the art can view worker tasks from any type of device (computer, smartphone) to monitor worker performance within a factory or workplace. Therefore, it would be obvious for one of ordinary skill in the art to combine the system seen in the combination of Masao and Zeng with the method of displaying information about the time it takes to complete a task found in Namioka 3 to achieve the invention of Claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nadler et al. (US 2023/0103112) teaches of a system to monitor the activity performed by the subject.
Walker et al. (US 2023/0145048) teaches of one or more implementations of estimating a pose of a subject by using an unconstrained video sequence that constitutes a physical record of the body features of the subject.
Namioka et al. (JP 2022046210 A) teaches of a learning device, processing device, learning method, posture detection model, program, and storage medium that can improve the detection accuracy of posture.
Jiang et al. (A Survey on Artificial Intelligence in Posture Recognition) teaches of the latest methods of posture recognition and review the various techniques and algorithms of posture recognition.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SORIE I KOROMA JR whose telephone number is (571)272-9259. The examiner can normally be reached Monday - Friday 8AM-6:00PM; Alternate Fridays Off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached at 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SORIE I KOROMA JR/Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
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