DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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.
Status of the Claims
This communication is in response to communications received on 1/2/24. Claim(s) none is/are amended, claim(s) 1-15 is/are cancelled, claim(s) 16-30 is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification. Therefore, Claims 16-30 is/are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 1/2/24 and 12/7/24 was/were considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application(s) filed in Germany on 7/6/21. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Response to Arguments
There are no arguments.
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(s) 16-30 is/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 pre-AIA the applicant regards as the invention.
Claim(s) 16-30 is/are rejected. Claim(s) 16, 29, and 30 state(s) the limitation “
determining a first result with the specified raw data and with a first model configured to predict results based on raw data from the first sensor;
determining a second result with the data representing the raw data of the second sensor and with a specified second model configured to predict results based on raw data from the second sensor;
determining whether or not the first result differs from the second result.”
Thus claim(s) 16, 29, and 30 is/are indefinite because it is unclear if a) the first two determining steps are intended to generate respectively a first and second result from the first and second model so a more logical difference can be determined from the third determining step and b) if the transformed raw sensor data of a second sensor is used. Appropriate correction/clarification is required. Claim(s) 17-28 is/are rejected because they depend on claim(s) 16, 29, and 30.
Claim(s) 16-30 is/are rejected. Claim(s) 16, 29, and 30 state(s) the limitation “
determining whether or not the first result differs from the second result; and
based on determining the first result differs from the second result, performing the following:
determining a training data point including the specified raw data and the second result, and
training the first model with training data including the training data point.”
Thus claim(s) 16, 29, and 30 is/are indefinite because it is unclear if the based on limitation is conditional. Appropriate correction/clarification is required. Claim(s) 17-28 is/are rejected because they depend on claim(s) 16, 29, and 30.
Claim(s) 16-30 is/are rejected. The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. Claim(s) 17-28 is/are rejected because they depend on claim(s) 16, 29, and 30.
Claim(s) 19-28 is/are rejected. Claim(s) 19, 20, 22, 25, 26, and 27 state(s) the limitation are unclear as they are based on the unclear portions of claim 16 as noted in the 112 rejections above. Appropriate correction/clarification is required. Claim(s) 21, 23-24 and 28 is/are rejected because they depend on claim(s) 19, 20, 22, 25, 26, and 27.
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.
Claim(s) 16-30 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 16, 29, and 30 that, under its broadest reasonable interpretation, is directed to a data based model copy.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 16-28 recites a series of steps) and system (claim(s) 29-30 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 1: transforming specified raw data from a first sensor into data representing raw data of a second sensor;
determining a first result with the specified raw data and with a first model configured to predict results based on raw data from the first sensor;
determining a second result with the data representing the raw data of the second sensor and with a specified second model configured to predict results based on raw data from the second sensor;
determining whether or not the first result differs from the second result; and
based on determining the first result differs from the second result, performing the following:
determining a training data point including the specified raw data and the second result, and
training the first model with training data including the training data point.
Claim(s) 29 and 30: same analysis as claim(s) 16.
Dependent claims 17-28 recite the same or similar abstract idea(s) as independent claim(s) 16, 29, and 30 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with large training data of models, and
mathematical relationships, mathematical formulas or equations, or mathematical calculations because the invention is directed to the application of mathematical processes as they are associated with models.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include sensors (claim(s) 16, 29, 30), computer (claim(s) 16), device (claim(s) 29), a non-transitory computer-readable medium, computer (claim 30), and sensor(s).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
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 - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0023-0025, 0028]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0023-0025, 0028]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 16-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cook (US 2013/0006906 A1) in view of Treiss (DE 10 2020 001 541 A1).
Regarding claim 16, 29, and 30, Cook teaches a computer-implemented method for generating a data-based model copy in a first sensor, comprising the following steps:
{a device configured to generate a data-based model copy in a first sensor, the device configured to: - claim 29}
{a non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for generating a data-based model copy in a first sensor, the instructions, when executed by a computer, cause the computer to perform the following steps: - claim 30}
determining a first result with the specified raw data and with a first model configured to predict results based on raw data from the first sensor;
determining a second result with the data representing the raw data of the second sensor and with a specified second model configured to predict results based on raw data from the second sensor [for the limitations above, see at least Figs. 1 and 2 and [0031, 0033-0034] “Example computing device 106 includes network interface(s) 202, processor(s) 204, and memory 206. … In the illustrated example, activity recognition module 212 includes a naïve Bayes classifier (NBC) 220, hidden Markov model 222, and conditional random field model 224. … Example activity model abstraction module 214 includes activity modeling module 226 and target activity partition module 228.”;
[0039] “At block 302, activity models are extracted from each of the available source spaces (i.e. existing smart environments). For example, activity recognition module 212 processes sensor data from each of the available source spaces to identify various activities. Activity modeling module 226 then builds activity models from the identified activities.”;
[0040] “At block 304, activity models are extracted from the target space (i.e., new smart environment). For example, target activity partition module 228 partitions sensor data received from the target space by location to identify possible activities. Because activity recognition has not yet been learned for the target space, the sensor data received from the target space is typically unlabeled. In an example implementation, the unlabeled data is partitioned based on sensor location. For example successive sensor events are first identified based on time. The successive sensor events are then grouped as activities based on sensor location. For example, if two successive sensor events are received from two sensors that are both located in the kitchen, then those sensor events will be grouped as a single activity. In contrast, if two successive sensor events are received from sensors in separate locations, then those successive sensor events will not be grouped as a single activity. For example, a first resident may trigger a sensor event in the kitchen, and a second sensor event may be triggered in close succession by a second resident in a bedroom. Because the sensors are not in the same location, they are not grouped as a single activity.”;
[0021] “Various types of machine learning models may be used for activity recognition including, but not limited to, naïve Bayes classifiers, decision trees, Markov models, and conditional random fields. In an example implementation, a naïve Bayes classifier (NBC), a hidden Markov model (HMM), and a conditional random field (CRF) model are used in various combinations.”];
determining whether or not the first result differs from the second result; and
based on determining the first result differs from the second result [see at least [0041] “At block 306, source activity templates are mapped to target activity templates. For example, mapping module 216 iteratively maps activities and sensors between the source spaces and the target spaces. Initial activity mappings are used to generate initial sensor mappings. The sensor mappings are then used to refine the activity mappings, which are then used to refine the sensor mappings, and so on.”;
[0042] “At block 308, target activities are labeled. For example, labeling module 218 assigns an activity label to each identified target activity.”;
[0050] “FIG. 6 illustrates an example process 306 for mapping activity templates from source spaces to activity templates in a target space.”;
[0051] “At block 602, a sensor mapping matrix is initialized. For example, sensor mapping matrix 230 is initialized to represent mappings between each source sensor and each target sensor. In an example implementation, a source sensor and a target sensor are mapped (initialized to a value of 1.0) if both sensors have the same location tag. A mapping between the source sensor and the target sensor is initialized to a value of 0 if the sensors have different location tags.”;
[0052] “At block 604, an activity mapping matrix is initialized. For example, activity mapping matrix 232 is initialized to represent mappings between each source activity template and each target activity template. Source activity templates are mapped to target activity templates based on spatial and temporal similarities between the activity templates.”;
[0053] “At block 606, sensor mapping probabilities are computed. For example, for each sensor pair in sensor mapping matrix 230, probability module 234 determines a probability that the sensors correspond to one another based on relationships between activities in which the two sensors appear, as defined in activity mapping matrix 232. For example if activity mapping matrix indicates a large number of mapped activities that include the two sensors, then the probability of the two sensors corresponding is increased. In contrast, if two sensors appear in very few mapped activities, then the probability of the two sensors corresponding may be decreased.”;
[0054] “At block 608, activity mapping probabilities are maximized based on the sensor mappings. For example, after the sensor mapping matrix is updated based on the calculated sensor mapping probabilities, probability module 234 performs a similar analysis against the sensor mapping matrix 230 to update the activity mapping matrix 232.”;
[0055] “At block 610, a determination is made as to whether or not sufficient iterations have been completed. For example, iterations may continue until no changes are perceived or until a predefined number of iterations is reached. If additional iterations are deemed appropriate (the “No” branch from block 610), then processing continues as described above with reference to block 606. If it is determined that sufficient interactions have already been completed (the “Yes” branch from block 610), then at block 612, template mapping is completed.”].
Cook doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such comparing sensor data, Triess discloses
transforming specified raw data from a first sensor into data representing raw data of a second sensor [see at least [0009] “The task is solved in particular by creating a method for transforming captured sensor data, such as images and/or point clouds, from a first data domain into a second data domain, whereby the following process steps are carried out:
a) Transforming sensor data, preferably acquired and/or generated, from a first data domain into a second data domain, using a trained cyclically generating adversarial network - sometimes also called a cycle-generating adversarial network, or cycle-GAN for short,”;
[0006] “However, there is still a need to provide a method by which sensor data from a first data domain - for example, old sensor - can be transformed into sensor data from a second data domain - for example, new sensor.”];
, performing the following:
determining a training data point including the specified raw data and the second result, and
training the first model with training data including the training data point [for the limitations above, see at least [0026] “According to a further development of the invention, it is provided that after receiving the sensor data from the second data domain according to process step a), the sensor data are transformed into the first data domain - preferably back - preferably again with the trained cyclically generating opponent network, and these transformed sensor data of the first data domain, i.e. those sensor data which were obtained by transforming the sensor data from the second data domain, are compared with the original sensor data from the first data domain - with regard to consistency - and in particular checked for consistency.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cook with Triess to include the limitation(s) above as disclosed by Triess. Doing so would improve Cook’s (Cook) knowledge transfer via improved machine learning in contrast to use of a matrix [see at least Triess [0007] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Cook and b) Triess and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 17, modified Cook teaches the method according to claim 16,
and Cook teaches wherein the raw data represent: (i) at least one time domain signal or at least one spectrum of: a radar sensor or LiDAR sensor or ultrasonic sensor or infrared sensor or acoustic sensor, or (ii) at least one position, or (iii) filtered data, or (iv) transformed data [see at least [0040] “At block 304, activity models are extracted from the target space (i.e., new smart environment). For example, target activity partition module 228 partitions sensor data received from the target space by location to identify possible activities. Because activity recognition has not yet been learned for the target space, the sensor data received from the target space is typically unlabeled. In an example implementation, the unlabeled data is partitioned based on sensor location. For example successive sensor events are first identified based on time. The successive sensor events are then grouped as activities based on sensor location. For example, if two successive sensor events are received from two sensors that are both located in the kitchen, then those sensor events will be grouped as a single activity. In contrast, if two successive sensor events are received from sensors in separate locations, then those successive sensor events will not be grouped as a single activity. For example, a first resident may trigger a sensor event in the kitchen, and a second sensor event may be triggered in close succession by a second resident in a bedroom. Because the sensors are not in the same location, they are not grouped as a single activity.”].
Regarding claim 18, modified Cook teaches the method according to claim 16,
and Cook teaches wherein: (i) the first result and/or the second result characterizes an object type or an estimate for a dimension of an object, or (ii) the first result and/or the second result indicates whether or not a blind sensor or clustering or an object has been detected [see at least [0018] “Based on the mappings, activities such as cooking or grooming may then be identified relatively quickly in the new smart environment based on data from the sensors in the existing smart environment being mapped to sensors in the new smart environment.”;
[0020] “Examples of specific activities that may be recognized include, but are not limited to, sleeping, bathing, bed to toilet transition, grooming, preparing/eating breakfast, watching TV, cleaning the bathroom, working at the computer, preparing/eating lunch, preparing/eating dinner, cleaning the apartment, or studying. Activity recognition may be implemented by comparing a pattern or sequence of detected actions with predetermined patters or sequences of actions corresponding to known activities. Activity recognition provides valuable insight regarding resident behavior, and may provide tools that will enable older adults to remain at home, rather than entering a supervised nursing facility. Activity recognition can also be utilized to enable a smart environment to provide context-aware services to the environment residents. For example, activity recognition may be used to prompt environment residents to take medication, feed pets, take out the trash, turn off appliances, or the like. In an example implementation, the activities that are identified may include well-known ADLs (Activities of Daily Living). In another implementation, the activities that are identified may also include additional activities that are not included in a list of ADLs, but that occur on a frequent basis.”].
Regarding claims 19-28, as noted by the 112 rejection above, the claims are unclear, interpreted as the method according to claim 16, and rejected as being analogous limitations to claim(s) 16 above.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamamoto – JP 2019204147 A (relevant because it teaches video comparison) as noted in IDS dated 12/17/24
Hazan et al. Dave – AdapterNet – learning input transformation for domain adaptation (relevant because it teaches fine tuning a neural network) as noted in IDS dated 1/2/24
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JAMES WEBB/Examiner, Art Unit 3624
Prosecution Insights