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 1-5 are currently pending in U.S. Patent Application No. 18/597,919 and an Office action on the merits follows.
Specification
The title of the invention (“Estimation Device”) is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Applicant is reminded of the proper content of an abstract of the disclosure. A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
With respect to the current abstract, it is under 50 words, and not fully descriptive of the purported improvements of the claimed invention; including but not limited to, the usage of iterative machine learning, as well as the rendering of output to replace tires.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
In paragraph 72, “types of processor” should read “types of processors”.
Appropriate correction is required.
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: 14B (Fig. 1).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the specification: 14.
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. 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 Interpretation
The claims will be read under the broadest reasonable interpretation standard outlined in MPEP § 2111.01.
The examiner interprets a “predetermined difference” as recited by claims 2 and 3 to include a difference of zero.
The phrase “render output to actually measure the depth of the groove” as recited by claim 2 is understood to include any notification to the user that an actual measurement should be taken, as well as automatic measurements taken by the system itself.
The phrase “re-learn the depth estimation model” as recited by claim 3 may include a finite amount of training instances.
The phrase “render output to replace the tire” as used in claims 4 and 5 is understood to include any indication by the device that the tire should be replaced, or automatic replacement of the tire by the system itself.
It is understood by the examiner, that in the context of measuring tire wear, groove depth and tread depth are considered interchangeable.
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 1-5 are rejected under 35 U.S.C. 101 because they are directed to ineligible patent subject matter. The claims are directed to the Abstract Idea groupings of mathematical calculations under MPEP § 2106.04(a)(2)(I) and mental processes under MPEP § 2106.04(a)(2)(III). These are judicial exceptions under Step 2A, Prong One of the framework outlined in the cases of Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 216, 110 USPQ2d 1976, 1980 (2014) and Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012). See MPEP § 2106(III).
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Step 1: The claims in question are directed to a device for “estimat[ing] the depth of a groove based on the image.” This falls under the statutory category of a machine. See MPEP 2106.03(I), “A machine is a "concrete thing, consisting of parts, or of certain devices and combination of devices." Digitech, 758 F.3d at 1348-49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)). This category "includes every mechanical device or combination of mechanical powers and devices to perform some function and produce a certain effect or result." Nuijten, 500 F.3d at 1355, 84 USPQ2d at 1501 (quoting Corning v. Burden, 56 U.S. 252, 267, 14 L. Ed. 683, 690 (1854)).” (Step 1: Yes).
Step 2A, Prong One: As explained in MPEP 2106.04(II), a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Here, each claim recites or depends upon the mathematical functions of depth calculation, and the mental processes of estimation, learning, and notification (Claim 1, “acquire an image of a tread of a tire that is installed at a vehicle,” “estimate a depth of a groove of the tire based on the image”; Claim 2, “acquire a travel history”, “estimate the depth of the groove based on the travel history”, “in a case in which a difference between the depth of the groove estimated based on the image and the depth of the groove estimated based on the travel history is greater than or equal to a predetermined difference, actually measure the depth of the groove”; Claim 3, “estimate the depth of the groove by inputting the acquired image to a depth estimation model learned using a set of the image and the depth of the groove as training data”, “acquire an actual measurement”, “re-learn the depth estimation model using a set of the image and the actual measurement as training data”; Claim 4, “render output to replace the tire”; Claim 5, “estimate, based on the image, whether or not damage has occurred at the tire; and in a case in which the processor has estimated the damage has occurred at the tire, render output to replace the tire”).
The claims are recited at a high level of generality and lack any specifics precluding such an analysis from being interpreted under the mental processes grouping of “practically performed in the mind” (see also MPEP § 2106.04(a)(2) identifying how e.g. a use of pen and paper, a ruler, or a computer as a tool (to assist in visually/mentally analyzing/observing acquired images/video) fails to preclude such an interpretation under the mental processes judicial exception). Activities such as “acquire a travel history” or “acquire an image” therefore may be performed mentally, even if they may require an additional tool for data collection. Similarly, basic computer imaging and measurement techniques do not elevate these claims past a mental process.
Regarding artificial intelligence, the claims of “re-learn the depth estimation model using a set of the image and the actual measurement as training data” and “render output to replace the tires” are also comparable to Claim 2 of Example 47 of the July 2024 PEG regarding subject matter eligibility (https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf). As stated therein, an artificial intelligence’s analyses, detections, and reinforcement learnings may be practically performed in the human mind. To the extent mathematical calculations are required to operate and train the artificial intelligence in image analysis, the separate judicial exception is also implicated.
As such, the usage of a computer to measure, visualize and calculate groove depth from an image does not elevate these claims beyond a mental process. (Step 2A, Prong One: Yes).
Step 2A, Prong Two: If Prong One of Step 2A is met, the examiner must consider (1) whether there are any ‘additional elements’ recited in the claim beyond the judicial exception, and (2) evaluate those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP § 2106.04(d).
Limitations the courts have found indicative of integration include: an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a); applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2); implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b); effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Limitations that the courts have found non-indicative of integration include: merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
As an additional note, ‘additional elements’ are generally limitations excluded from interpretation under the Abstract Idea groupings, and may comprise portions of limitations otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any ‘determination’ that may be made mentally accompanied by the use of a neural network and/or generic computer hardware considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection’ of data (i.e. image acquisition(s)), be they images for training any learning model and/or data/images visually observable/ evaluated by a user/operator, also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired (e.g. the use of sensors or cameras for acquiring said imagery broadly). The same determination holds for dependent claims that serve to limit the collection of data/images (by means of what is collected based on recited conditions) and/or introduce limitations generally linking to a field of use.
None of the instant claims appear to explicitly/clearly capture/recite any disclosed improvement in technology (see MPEP 2106.05(a), with note that ‘functioning of a computer’ concerns functions integral to the way a computer operates and not ‘functions’ that a generic computer can be programmed/adapted to perform (see also 2106.05(f))) and any ‘additional elements’, even when considered in combination, fail to integrate at Prong Two of Step 2A accordingly. Integration in view of subsection (a) requires an identification of the manner in which the improvement is achieved, to be explicitly and specifically recited in the claims, as ‘additional elements’ precluded from interpretation under any of the Abstract Idea groupings (since the improvement cannot be to the exception itself). With reference to MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981))
As applicable here, additional limitations not directed to a judicial exception fail to
integrate at Prong Two of Step 2A. Claim 3 recites a “depth estimation model learned using a set of the image and the depth of the groove as training data” that is “re-learn[ed]…using a set of the image and the actual measurement value as training data.” The incorporation of a re-learning depth estimation model does little more than generally link the judicial exceptions of mental processes and mathematical calculations to a field-of-use and technological environment – artificial intelligence. See MPEP § 2106.05(h); See page 9, paragraph 2 of the July 2024 PEG cited above.
Claim 2 and 4 recite “render[ing of an] output to actually measure the depth of the groove,” and “render[ing of an output] to replace the tire” respectively. These limitations constitute insignificant extra-solution activity under MPEP § 2106.05(g). Specifically, the limitation amounts to no more than necessary data gathering and outputting, under rationale 3 of MPEP § 2106.05(g).
Even when viewed in combination, any additional elements present do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: No), and the claims are directed to the judicial exception. (Revised Step 2A: Yes [Wingdings font/0xE0] Step 2B).
Step 2B: If Prong Two of Step 2A is not met, the examiner must consider whether the claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any ‘additional element’, or combination of additional elements, adds an inventive concept to the claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also requires considering whether the claims feature any “specific limitation(s) other than what is well-understood, routine, conventional activity in the field” (WURC) (MPEP § 2106.05(d)). Such a limitation if specifically recited however, must still be excluded from interpretation under any of the Abstract Idea groupings. Step 2B further requires a re-evaluation of any additional elements drawn to extra-solution activity in Step 2A (e.g. gathering images, rendering output) – however no limitations appear directed to any novel collection or output generation per se. Limitations not indicative of an inventive concept/‘significantly more’ include those that are not specifically recited (instead recited at a high level of generality), those that are established as WURC (a plurality of cited references serve to evidence the WURC nature of ‘analysis’ based at least in part on corroborating/ additional ground data), and/or those that are not ‘additional elements’ by nature of their analysis at Prong One of Step 2A (i.e. directed to the exception – see above re. deciding that a second acquisition may be advantageous/desired). The July 2024 PEG describes that an improvement/ inventive concept (for ‘significantly more’ determination(s)) cannot be to the judicial exception itself.
The claim(s) in question recite little beyond those limitations recited at a high level of generality and falling under e.g. the mental processes Abstract Idea grouping, and would monopolize the exception accordingly. The additional limitations image analysis, machine learning, and output rendering as recited are WURC, as evidenced by the body of prior art cited by the examiner in this office action. (Step 2B: No).
Claim Rejections - 35 USC § 102
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.
Claim 1, 4, and 5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Iwata (US 20250238942 A1) (Hereinafter “Iwata”).
As to claim 1, Iwata discloses a processor (Iwata, Paragraph 166) coupled to memory (Iwata, Paragraph 94), configured to acquire an image of a tread of a tire (Iwata, Paragraph 6) that is installed at a vehicle (Iwata, Paragraph 113 “It is assumed that the vehicle is lifted up in a state in which the tire T is attached to each of the wheels”), and estimate a depth of a groove of the tire based on the image (Iwata, Paragraph 109).
As to claim 4, Iwata discloses “the estimation device according to claim 1, wherein, in a case in which the depth of the groove estimated based on the image is less than a predetermined depth, the processor is configured to render output to replace the tire.” (Iwata, Paragraph 109, “In the present embodiment, the estimation result of the wear amount of the tread is an index indicating the remaining depth of the main groove of the tread in stages”; Iwata, Paragraphs 131-132, “In the determination table 134, data of the corresponding cell is “A3”, and in this case, it is recommended to replace the tire T. As described above, according to the determination according to the determination table 134, the estimation result of the uneven wear and the estimation result of the wear amount are comprehensively considered, and if one is level 1 and the other is level 2 or above, it is determined that the countermeasure according to the larger degree is recommended”; Iwata, Fig. 9A):
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As to claim 5, Iwata discloses an “estimat[ion], based on the image, [of] whether or not damage has occurred at the tire; and in a case in which the processor has estimated that damage has occurred at the tire, [the] render[ing of an] output to replace the tire.” (Iwata, Paragraph 4, “If the uneven wear is left unattended, the tire is likely to be damaged”; Iwata, Paragraphs 92, “The determination unit 10C determines whether tire replacement or tire rotation is necessary based on the outputs derived by the derivation unit 10B and the determination table 134. The screen generation unit 10D generates a feedback screen that displays a determination result or the like by the determination unit 10C”; Iwata, Fig. 9A).
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 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Iwata in view of Lemaire et. al (US 2025/0209859 A1) (Hereinafter “Lemaire”) and Raje (US 2022/0326703 A1) (Hereinafter “Raje”).
As to claim 2, Iwata teaches the elements of claim 1 by which claim 2 depends, as discussed in the section above. Iwata further teaches the acquiring of a vehicle travel history (Iwata, Paragraph 2, “The machine learning model collects and uses sensor data such as…a total travel distance obtained from in-vehicle sensors”). Iwata does not teach an estimation of depth based on vehicle travel history, or the rendering of an output to actually measure the depth of the groove should the groove depth be greater than or equal to a predetermined difference.
However, Lemaire teaches the estimation of depth based on vehicle travel history (Paragraph 71, “As used herein, the “remaining useful life” (or “RUL”) of an identified tire refers to its mileage potential as a function of the values of the parameters influencing its longevity. The remaining useful life can be determined continuously or at regular, predefined or sporadic intervals by gathering the data corresponding to the parameters influencing the lifetime of the identified tire. Based on the gathered data, the method for estimating RUL of the invention can define a current state of use of the identified tire in order to determine its remaining useful life”; Paragraph 70, “The state of use can be in the form of a unit value such as the remaining mileage as a function of the tread height (without having to measure the tread depth).”)
Lemaire does not teach the rendering of an output to actually measure the depth of the groove, should the groove depth be greater than or equal to a predetermined difference. However, Raje provides for the same (Raje, Paragraph 35, “For example, the data processing system 110 can predict when the tire will be worn out (e.g., treads with a depth of fewer than 2/34 inches) at a time interval (e.g., within a week or a month from the prediction date). The notification can include an indication to the operator to check the tread of the tire. This notification can appear prior to or during the predicted time interval. The notification can include interactive elements, such as a button or slider for the operator to provide feedback or confirmation. In this example, the operator can confirm that the tread depth is below the threshold or provide feedback that the tread depth is not below the threshold.”).
Iwata, Lemaire, and Raje are analogous to the claimed invention, because they are in the field of tire degradation estimation. Iwata captures the underlying means of estimating tire degradation through image analysis and machine learning. Lemaire and Raje constitute known techniques for further refinement of a tire degradation model. A person of ordinary skill in the art as of the effective filing date of the claimed invention could readily consult Lemaire and Raje, to adopt their advances into the base system of Iwata.
Lemaire provides for an additional means of predicting tire degradation via a mileage function. A mileage function can easily be integrated into the system of Iwata without resulting in any dissonance. Mileage information is already collected by Iwata (Iwata, Paragraph 2 “The machine learning model collects and uses sensor data such as…a total travel distance obtained from in-vehicle sensors”). Mileage information is advantageous, to establish clear intervals at which a tire would need replacement.
Raje applies a system of user notification to prompt actual measurement of the tire. This system can be readily integrated to ensure the mileage function of Lemaire and the image estimation functions of Iwata are accurate. Iwata encourages such a validation process (Iwata, Paragraph 145 “In the present embodiment, the image for learning and these labels are combined by a person who has confirmed the actual tire in the image for learning.”).
Thus, a person of ordinary skill in the art would be motivated to combine these references to increase the scope and accuracy of the prediction model, as of the effective filing date of the claimed invention.
As to claim 3, Iwata teaches the elements of claim 1 by which claim 3 depends, as discussed in the section above. Iwata further teaches the estimation of groove depth by inputting the acquired image to a depth estimation model learned using a set of the image and the depth of the groove as training data (Iwata, Paragraph 89; Iwata, Fig. 2), the acquiring of an actual measurement value of the groove depth (Iwata, Paragraph 156 “In the present embodiment, the wear amount level corresponding to the remaining depth of the main groove of the tread is determined for each type of the tire T. An appropriate wear amount level is selected according to the depth of the main groove measured in the actual tire in the image for learning, and a label indicating the wear amount level is combined with the image for learning. In the present embodiment, a person checks an actual tire, selects a portion of a main groove that is worn most (shallower), and measures the depth of the groove at this portion.”), and the re-learning of the depth estimation model using a set of the image and the actual measurement as training data:
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(Iwata, Paragraphs 145-157 (generally); Iwata, Paragraph 145, “In any of these data for learning, the correct answer data is a label of an index indicating a degree of the uneven wear. That is, the correct answer data is a label of one of the uneven wear level 1, the uneven wear level 2, and the uneven wear level 3. In the present embodiment, the image for learning and these labels are combined by a person who has confirmed the actual tire in the image for learning; Iwata, Fig. 10; Iwata, Paragraph 149 “In subsequent step S53, the learning unit 10E inputs K images for learning included in the sample data to the first machine learning model 130A and derives an output from the first machine learning model 130A. The output is data corresponding to the correct answer data combined with each of the input K images for learning, and in the present embodiment, is a probability corresponding value corresponding to a label of the uneven wear level 1, the uneven wear level 2, or the uneven wear level 3.”)
This element is also taught by Lemaire. Note step 206 of the below figure, which is reiterated in all instances of the flowchart:
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(Lemaire, Fig.2; Lemaire, Paragraph 84, “It is understood that a measurement of the tread depth can be included in this data to serve as a basis for validation, but this measurement is not critical for the completion of the method; Lemaire, Paragraph 101 “The created database can include images of worn tires in known states of use and the corresponding number of completed journeys. Therefore, the learning database can include expected images (and therefore data) corresponding to the profiles (3D, 2D, 1D) of the worn tires and the journeys of the associated vehicles (including the number of miles covered during these journeys)”; Lemaire, Paragraph 110 “With this estimation algorithm, physical measurements are not necessary for estimating the removal of an identified tire due to its complete wear at the end of its life. It is understood that it is possible to use the tread pattern depth associated with each mileage to improve the accuracy of the algorithm that is used, if the data is available”).
The remaining elements of claim 2 incorporated into claim 3 are taught by Lemaire and Raje, discussed in the rejection of claim 2 above.
Claim 3 continues the validation process of claim 2, by introducing the actual measurements of groove depth into the learning model. For the elements of claim 2 by which claim 3 depends, see the above rejection of claim 2. As for the further limitations of claim 3, they are present in the base reference of Iwata. Lemaire additionally evidences the same concepts of actual data validation and re-learning of the depth estimation model. Unlike Iwata, the training in Lemaire does not stop when all “epochs” (Iwata, Fig. 10, step S56) are finished, but is instead continuous (Lemaire, Fig. 2, training step 206 is re-iterated upon acquisition of new training samples 216; Lemaire, Paragraph 97, “In order to train the prediction model, this data is stored (for example, in a database 110 of the system 100) (see FIG. 1), and it is updated throughout the duration of the method (either on a continuous or intermittent basis).”). It would have been obvious, before the effective filing date of the claimed invention, to modify the system/method of Iwata so as to perform continuous re-learning as taught by Lemaire, such that the model accuracy may be further improved with newly acquired reference samples beyond any set period of time.
Additional References
Additionally cited references (see attached PTO-892) otherwise not relied upon above have been made of record in view of the manner in which they evidence the general state of the art.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH WILLIAM BOYAR whose telephone number is (571) 272-8392. The examiner can normally be reached 8:30 – 5:00 EST, Monday – Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NOAH W BOYAR/
Examiner, Art Unit 2669
/CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669