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 .
Specification
35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, requires the specification to be written in “full, clear, concise, and exact terms.” The specification is replete with terms which are not clear, concise and exact. The specification should be revised carefully in order to comply with 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112. Examples of some unclear, inexact or verbose terms used in the specification are:
¶ 0014 of the applicant’s specification filed 5 September, 2024 states “According to an embodiment, during a learning process, for each topic of the set of topics, topic information is found within the high dimensional sparse binary representation is detected, so that topic information related to different topics are decorrelated.” This paragraph is unclear and does not provide any understanding of what “decorrelated” means with respect to different topics and the grammatic errors make the paragraph difficult to understand. This paragraph needs revision.
¶ 0015 states “According to an embodiment, the third fraction is of the order of the product of multiplication of the second fraction by the third fraction.”, this is not explained or shown in a formula and is confusing as worded. Is this intended to say that the third fraction is equal to the value of the second fraction multiplied by the third fraction? If so, how is the third fraction equal to itself multiplied by another fraction?
Drawings
The drawings are objected to because figure 4 shows a plurality of lines extending from CD 510. Two of the lines connect to PO-1 505-1 and PO-K 505-K, however found other lines extend and do not connect to anything. Additionally there are a plurality of vertical lines in the middle of the figure with no reference numbers or indication of what they are. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 1 objected to because of the following informalities:
Claim 1 states in line 7 – 8 “each of the selected set of topics”. The examiner is unsure what the “selected set of topics” is intended to refer to as there is no step in claim 1 of selecting a set of topics. There is reference to “a set of topics” in which a topic is selected from for the sparse binary representation of the road element, however there is not any reference to selecting a set of topics, nor is there a plurality of a set of topics which would justify “each of the selected set of topics”.
Claim 1 states in line 8 “the selected topic corresponding to a reduced space, each of the selected set of topics…”, the examiner believes this should be amended to say “the selected topic corresponding to a reduced space, and each of the selected set of topics…” for clarity.
Claim 1 states in line 8 “the selected topic corresponding to a reduced space”, the examiner is unsure what this is intended to mean. Referencing the applicant’s specification ¶ 0052 step B states the same claim language without any more information. ¶ 0053 states “the dimension of the initial embedding space is determined by the size of the sparse binary representation and may exceed one thousand, ten thousands and the like”. However, the “reduce space” is not clear if this is a reduced dimensionality space or something else.
Claim 11 objected to because of the following informalities:
Claim 11 states in line 8 – 9 “each of the selected set of topics”. The examiner is unsure what the “selected set of topics” is intended to refer to as there is no step in claim 1 of selecting a set of topics. There is reference to “a set of topics” in which a topic is selected from for the sparse binary representation of the road element, however there is not any reference to selecting a set of topics, nor is there a plurality of a set of topics which would justify “each of the selected set of topics”.
Claim 11 states in line 8 – 9 “the selected topic corresponding to a reduced space, each of the selected set of topics…”, the examiner believes this should be amended to say “the selected topic corresponding to a reduced space, and each of the selected set of topics…” for clarity.
Claim 11 states in line 8 – 9 “the selected topic corresponding to a reduced space”, the examiner is unsure what this is intended to mean. Referencing the applicant’s specification ¶ 0052 step B states the same claim language without any more information. ¶ 0053 states “the dimension of the initial embedding space is determined by the size of the sparse binary representation and may exceed one thousand, ten thousands and the like”. However, the “reduce space” is not clear if this is a reduced dimensionality space or something else.
Appropriate correction is required.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When reviewing independent claim 11, and based upon consideration of all of the relevant factors with respect to the claim as a whole, claims 1 - 20 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. The Examiner will analyze claim 11, and similar rationale applies to independent claim 1. The rationale, under MPEP § 2106, for this finding is explained below:
The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a manufacture since the claim is directed to a non-transitory computer readable medium that stores instructions executable by a processing circuit.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
The Examiner interprets that the judicial exception applies since claim 11 limitations of “obtaining, at a machine learning process, a sparse binary representation corresponding to an initial embedding space of a road element captured in a sensed information unit”, “selecting, by checking values of topic information of the sparse binary representation using the machine learning process, a topic, from a set of topics respectively characterized in the initial embedding space, the selected topic corresponding to a reduced space, each of the selected set of topics determined in decorrelation from another topic based on at least in part a measurement of an entropy distribution of bits of the sparse binary representation”, and “determining, using the selected topic, a topic based representation of the road element for classification.” are directed to an abstract idea. The claim is related to mathematical concepts and mental processes by performing a step of obtaining a sparse binary representation which is a form of mathematic transformation of the initial embedding space, and the steps of selecting a topic and determining a topic being decision based steps which could easily be performed within the mind of a person. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The Examiner interprets that the claim 11 limitations do not provide additional elements or combination of additional elements to a practical application since the claim is generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). See, MPEP §2106.04(a), Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). OR Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. In such a case, after making the appropriate rejection (see MPEP § 2106.07 for more information on formulating a rejection for lack of eligibility), it is a best practice for the examiner to recommend an amendment, if possible, that would resolve eligibility of the claim.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
The Examiner interprets that the claims do not amount to significantly more since the claim states steps which comprise only the judicial exception, and merely recite “a machine learning process” which does not amount to significantly more.
Furthermore, the generic computer components of the non-transitory computer readable medium storing instructions, a processing circuit, and a machine learning process recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Claims 2 – 10 and 12 – 20 depending on the independent claim include all the limitation of the independent claim. The Examiner finds that claim 12, 13, and 15 - 20 does not state significantly more since the claim only recites “using the selected topic, a topic based representation of the road element for classification.” In claims 2 and 12; “classifying the road element in accordance with the topic based representation.” in claims 3 and 13; “generating, based on a classification of the road element, a driving related output with respect to a vehicle.” in claims 4 and 14; “selecting another topic, from the set of topics, for classification of another road element.” In claims 5 and 15; “exclusively determining an association for the selected topic” in claims 6 and 16; “determining a probability of an association for the selected topic.” In claims 7 and 17; “wherein the determining of the probability is based on topic rules that associate a probability for matches and partial matches between the values of the topic information of the sparse binary representation and expected values of the topic information.” In claims 8 and 18; “applying an entropy based unsupervised learning process for learning the entropy distribution of the sparse binary representation.” In claim 9 and 19; and “identifying the set of topics by applying a total correlation process.” In claims 10 and 20.
Thus, claims 2 – 10 and 12 - 20 recite the same abstract idea and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more.
Therefore, the Examiner interprets that the claims are rejected under 35 U.S.C. 101.
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 1 and 11 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.
Claims 1 and 11 recite the limitation "the selected set of topics" in lines 7 – 8 and 8 – 9 respectively. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 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) the invention was known or used by others in this country, or patented or described in a printed publication in this or a foreign country, before the invention thereof by the applicant for a patent.
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 102(a)(2) as being anticipated by Li et al (U.S. Patent Publication No. 2024/0087222 A1, hereinafter “Li”).
Regarding claim 1, Li teaches a method of decorrelated topic based representation of road elements for classification, the method comprising:
obtaining, at a machine learning process, a sparse binary representation corresponding to an initial embedding space of a road element captured in a sensed information unit (¶ 0043: The transformer-based framework 100 is configured to predict a dense semantic scene within a specific volume (e.g., a volume in front of an autonomous vehicle) based on one or more 2D images.; ¶ 0044: In particular, one or more input images 102 are provided as input to a first stage 110 of the transformer-based framework 100, which processes the images 102 using a depth estimation network to generate a set of query proposals QP 104. The query proposals QP 104 estimate the location of objects that appear in the images 102 within a sparse binary voxel grid.); and
selecting, by checking values of topic information of the sparse binary representation using the machine learning process, a topic associated with the sparse binary representation, from a set of topics respectively characterized in the initial embedding space (¶ 0045: A transformer extracts keys K and values V from the set of feature maps and processes them according to a query proposal QP 104 using a cross-attention mechanism to generate a set of updated query proposals
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and a mask token 106 are then used to populate a voxel feature map… The dense voxel feature map is then up-sampled and processed by a lightweight fully-connected network to generate a dense semantic voxel grid 108 that assigns a semantic class to each voxel of a defined volume at a particular output space.; Examiner’s note: This limitation is extremely broad and the examiner understands this limitation to include identifying a topic/category/label/classification for each binary representation using a machine learning model which is understood to be performed by any classification machine learning model.), the selected topic corresponding to a reduced space (¶ 0045: The dense voxel feature map is then up-sampled and processed by a lightweight fully-connected network to generate a dense semantic voxel grid 108 that assigns a semantic class to each voxel of a defined volume at a particular output space.; Examiner’s note: As noted in the objection in the “claim objections” section, it is unclear what “a reduced space” is intended to refer to. The system as described by Li performs an upsampling on the voxel feature map, which is understood that the features are identified in a smaller size voxel feature map before being upsampled. Additionally, Li describes encoder-decoder convolutional neural networks which are understood to change the dimensions of the input data throughout the layers of the encoder and decoder.), each of the selected set of topics determined in decorrelation from another topic based on at least in part a measurement of an entropy distribution of bits of the sparse binary representation (Eq. 6; ¶ 0052: The feature extraction network 124 may be pre-trained using an existing training dataset to extract a number of features from the image(s) 102.; ¶ 0057: The framework 100 is trained end-to-end with a weighted cross-entropy loss. Specifically, the ground truth
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defined at time t represents a multi-class semantic voxel grid.; Examiner’s note: As noted in the objection in the “claim objections” section, it is not clear what “determined in decorrelation” means. The specification provides no definition for this term and does not make clear how this is intended to be defined in the context of this application. As such the examiner is interpreting this limitation under broadest reasonable interpretation to include that the topics are learned using any entropy learning function during the model’s training.).
Regarding claim 2, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising determining, using the selected topic, a topic based representation of the road element for classification (¶ 0045: The dense voxel feature map is then up-sampled and processed by a lightweight fully-connected network to generate a dense semantic voxel grid 108 that assigns a semantic class to each voxel of a defined volume at a particular output space. (emphasis added)).
Regarding claim 3, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising classifying the road element in accordance with the topic (¶ 0045: The dense voxel feature map is then up-sampled and processed by a lightweight fully-connected network to generate a dense semantic voxel grid 108 that assigns a semantic class to each voxel of a defined volume at a particular output space. (emphasis added)).
Regarding claim 4, Li teaches the method according to claim 3.
Additionally, Li teaches further comprising generating, based on the classifying of the road element, a driving related output with respect to a vehicle (¶ 0058: For example, the apparatus 200 may be incorporated into an autonomous vehicle, such as autonomous vehicle described in further detail in FIGS. 7 A-7B, which may use the semantic voxel grid 108 generated using the framework 100 in a number of related tasks including, but not limited to, emergency braking, pedestrian detection, collision avoidance, route planning, lane departure warning, traffic sign assist, intelligent headlamp control, or any other type of advanced driver assistance systems (ADAS) functions.¶ 0065: For example, the semantic voxel grid 108 can be used by an obstacle avoidance algorithm of an autonomous vehicle that is designed to detect objects in the path of the vehicle or objects that have a potential to move into the path of the vehicle, and change the driving state of the vehicle according to a number of rules.).
Regarding claim 5, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising selecting another topic, from the set of topics, for classification of another road element (¶ 0045: A transformer extracts keys K and values V from the set of feature maps and processes them according to a query proposal QP 104 using a cross-attention mechanism to generate a set of updated query proposals
Q
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P
. The updated proposals
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and a mask token 106 are then used to populate a voxel feature map… The dense voxel feature map is then up-sampled and processed by a lightweight fully-connected network to generate a dense semantic voxel grid 108 that assigns a semantic class to each voxel of a defined volume at a particular output space.; Examiner’s note: Examiner understands that a semantic class is assigned to each voxel for each location of a query proposal.).
Regarding claim 6, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising exclusively determining an association for the selected topic (¶ 0203: In at least one embodiment, a DLA may be used to run any type of network to enhance control and driving safety, including for example and without limitation, a neural network that outputs a measure of confidence for each object detection.).
Regarding claim 7, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising determining a probability of an association for the selected topic (In at least one embodiment, a DLA may be used to run any type of network to enhance control and driving safety, including for example and without limitation, a neural network that outputs a measure of confidence for each object detection.).
Regarding claim 8, Li teaches the method according to claim 7.
Additionally, Li teaches wherein the determining of the probability is based on topic rules that associate a probability for matches and partial matches between the values of the topic information of the sparse binary representation and expected values of the topic information (¶ 0203: In at least one embodiment, a DLA may be used to run any type of network to enhance control and driving safety, including for example and without limitation, a neural network that outputs a measure of confidence for each object detection. In at least one embodiment, confidence may be represented or interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. In at least one embodiment, a confidence measure enables a system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections. In at least one embodiment, a system may set a threshold value for confidence and consider only detections exceeding threshold value as true positive detections.).
Regarding claim 9, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising applying an entropy based unsupervised learning process for learning the entropy distribution of the sparse binary representation (Eq. 6; ¶ 0056: The framework 100 is trained end-to-end with a weighted cross-entropy loss.; ¶ 0076: Implementing the neural network model can include determining a set of parameters for the neural network model through, e.g., supervised or unsupervised training of the neural network model as well as, or in the alternative, performing inference using the set of parameters to process novel sets of inputs.).
Regarding claim 10, Li teaches the method according to claim 1.
Additionally, Li teaches further comprising identifying the set of topics by applying a total correlation process (¶ 0076: Implementing the neural network model can include determining a set of parameters for the neural network model through, e.g., supervised or unsupervised training of the neural network model as well as, or in the alternative, performing inference using the set of parameters to process novel sets of inputs (emphasis added).).
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of manufacture claim 11 while noting that the rejection above cites to both method and manufacture disclosures.
For the manufacture limitations of claim 11 see Li’s teaching on:
A non-transitory computer readable medium for decorrelated topic based representation of road elements for classification, the non-transitory computer readable medium stores instructions executable by a processing circuit for (¶ 0024: In accordance with a third aspect of the present disclosure, a non-transitory computer-readable media is disclosed storing computer instructions that, responsive to being executed by one or more processors, cause a device to perform semantic scene completion)…; and
Determining, using the selected topic, a topic based representation of the road element for classification (¶ 0045: The dense voxel feature map is then up-sampled and processed by a lightweight fully-connected network to generate a dense semantic voxel grid 108 that assigns a semantic class to each voxel of a defined volume at a particular output space (emphasis added).).
The rejection of method claim 2 above applies mutatis mutandis to the corresponding limitations of manufacture claim 12 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 3 above applies mutatis mutandis to the corresponding limitations of manufacture claim 13 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 4 above applies mutatis mutandis to the corresponding limitations of manufacture claim 14 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 5 above applies mutatis mutandis to the corresponding limitations of manufacture claim 15 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 6 above applies mutatis mutandis to the corresponding limitations of manufacture claim 16 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 7 above applies mutatis mutandis to the corresponding limitations of manufacture claim 17 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 8 above applies mutatis mutandis to the corresponding limitations of manufacture claim 18 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 9 above applies mutatis mutandis to the corresponding limitations of manufacture claim 19 while noting that the rejection above cites to both method and manufacture disclosures.
The rejection of method claim 10 above applies mutatis mutandis to the corresponding limitations of manufacture claim 20 while noting that the rejection above cites to both method and manufacture disclosures.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shaag et al (U.S. Patent Publication No. 2022/0309693 A1) teaches usage of a LiDAR depth image estimation to detect and determine objects in a vehicles field of view using machine vision algorithms.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW JONES whose telephone number is (703)756-4573. The examiner can normally be reached Monday - Friday 8:00-5:00 EST, off Every Other Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B. JONES/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
